bishengWeb/public/locales/en/bs.json

{
    "title": "NPCs",
    "prompt": "Confirmation",
    "login": {
        "slogen": "Convenient, Flexible, Reliable Enterprise-Level Large Model Application Development Platform",
        "account": "Account",
        "password": "Password",
        "confirmPassword": "Confirm Password",
        "noAccountRegister": "Don't have an account? Register",
        "haveAccountLogin": "Already have an account? Login",
        "loginButton": "Log In",
        "registerButton": "Register",
        "document": "Documentation",
        "pleaseEnterAccount": "Please enter your account",
        "pleaseEnterPassword": "Please enter your password",
        "accountTooShort": "Account is too short",
        "passwordTooShort": "Password is too short, at least 8 characters",
        "passwordError": "The password must include upper and lower case letters, numbers, and characters!",
        "passwordMismatch": "Passwords do not match",
        "registrationSuccess": "Registration successful. Please enter your password to log in",
        "pleaseEnterCaptcha": "Please enter captcha"
    },
    "menu": {
        "app": "App",
        "skills": "Skills",
        "knowledge": "Knowledge",
        "models": "Models",
        "system": "System",
        "themeSwitch": "Theme Switch",
        "document": "Documentation",
        "logout": "Logout",
        "logoutDescription": "Log out",
        "forBestExperience": "For the best experience, please access this website on a PC",
        "onlineDocumentation": "Online Documentation"
    },
    "system": {
        "userManagement": "User Management",
        "roleManagement": "Role Management",
        "systemConfiguration": "System Configuration",
        "username": "Username",
        "confirmDisable": "Confirm disabling this user?",
        "roleSelect": "Select Role",
        "roleList": "Role List",
        "confirmText": "Are you sure you want to delete",
        "roleName": "Role Name",
        "skillAuthorization": "Skill Authorization",
        "knowledgeAuthorization": "Knowledge Base Authorization",
        "skillName": "Skill Name",
        "creator": "Creator",
        "usePermission": "Use Permission",
        "managePermission": "Manage Permission",
        "roleNamePrompt": "Role name cannot exceed 50 characters",
        "roleNameRequired": "Role name is required",
        "roleNameExists": "Role name already exists",
        "parameterConfig": "Parameter Configuration",
        "language": "Language"
    },
    "skills": {
        "manageTemplate": "Manage Skill Templates",
        "createNew": "Create New",
        "manageProjects": "Manage your personal projects here, including skill deployment and editing",
        "skillSearch": "Skill Search",
        "confirmDeleteSkill": "Confirm the deletion of this skill?",
        "backToSkillList": "Back to Skill List",
        "skillTemplateManagement": "Skill Template Management, templates are visible to all users, supports drag and drop sorting, and deletion",
        "templateName": "Template Name",
        "templateDescription": "Template Description",
        "confirmText": "Are you sure you want to delete this skill template?",
        "skillSettings": "Skill Settings",
        "basicInfo": "Basic Information",
        "skillName": "Skill Name",
        "description": "Description",
        "parameterInfo": "Parameter Information",
        "advancedConfiguration": "Advanced Configuration",
        "nextStep": "Next Step, Advanced Configuration",
        "skillNameRequired": "Please enter a skill name",
        "skillNameTooLong": "Skill name is too long, should not exceed 30 characters",
        "skillNameExists": "This name already exists",
        "skillDescRequired": "Please enter a skill description",
        "skillDescTooLong": "Skill description is too long, should not exceed 200 characters",
        "errorTitle": "Key Information Error",
        "onlineFailure": "Online failure",
        "custom": "Custom",
        "skillTemplate": "Skill Template",
        "skillTemplateChoose": "You can choose a template from here to start, or create an advanced template",
        "createTemplate": "Create Template",
        "skillDescriptionRequired": "Please enter a skill description",
        "skillDescriptionTooLong": "Skill description is too long, should not exceed 200 characters",
        "keyInfoErrorTitle": "Key Information Error",
        "createSuccessTitle": "Skill created successfully",
        "createFailureTitle": "Creation failed",
        "createdBy": "Created by",
        "offline": "Offline",
        "online": "Online",
        "guideWords": "Guide Words"
    },
    "chat": {
        "newChat": "New Chat",
        "selectChat": "Select a skill to start",
        "inputPlaceholder": "Enter your question",
        "uploadFileTooltip": "Upload File",
        "sendTooltip": "Send",
        "forms": "Forms",
        "skillTempsTitle": "Skill Selection",
        "skillTempsDesc": "Choose an online skill you want to use",
        "skillDeleted": "This skill has been deleted",
        "networkError": "Network connection error. Please try the following methods",
        "networkErrorList1": "Do not operate too quickly",
        "networkErrorList2": "Refresh the page",
        "networkErrorList3": "Check if the backend is running",
        "buildError": "It seems you are missing some configurations",
        "connectionbreakTip": "Connection Abnormal Disconnect:",
        "connectionbreak": "Network Disconnected!",
        "copyTip": "Content copied",
        "noAccess": "Due to insufficient permissions, this answer has excluded content that you are not authorized to view",
        "source": "Reference Source",
        "file": "File",
        "filePrsing": "File is currently being parsed",
        "sourceTooltip": "Source Paragraph",
        "filterLabel": "Filter Labels",
        "tooltipText": "The system generates key information tags automatically based on the answers, and you can also manually add or delete labels. The system calculates the relevance of various files and paragraphs based on these tags.",
        "customLabel": "Custom",
        "addCustomLabel": "+ Custom",
        "sourceDocumentsLabel": "Source Documents",
        "downloadPDFTooltip": "Download Dual-Layered PDF",
        "downloadOriginalTooltip": "Download Original File",
        "noMatchedFilesMessage": "No matched source files",
        "fileStorageFailure": "File storage failure!",
        "confirmDeleteChat": "Confirm deletion of this chat？",
        "roundOver": "This round is over",
        "chatDialogTip": "Set the input variables defined in the prompt template. Interact with agents and chains.",
        "feedback": "Feedback",
        "feedbackRequired": "Feedback is required"
    },
    "model": {
        "modelConfiguration": "Model Configuration",
        "modelName": "Model Name",
        "modelConfigLabel": "Model Configuration",
        "modelConfigExplanationLink": "Model Configuration Parameter Explanation",
        "jsonFormatError": "JSON format error",
        "onlineStatus": "Online",
        "offlineStatus": "Offline",
        "exceptionStatus": "Exception",
        "warningTooltip": "Processing Exception",
        "inProgressOnlineStatus": "Online in Progress",
        "inProgressOfflineStatus": "Offline in Progress",
        "confirmModelOffline": "Confirm taking this model offline? skills using this model will not work properly",
        "confirmOfflineButtonText": "Offline",
        "modelManagement": "Model Management",
        "modelFineTune": "Model Finetune",
        "refreshButton": "Refresh",
        "gpuResourceUsage": "GPU Resource Usage",
        "modelCollectionCaption": "Model Collection",
        "machine": "Machine",
        "serviceAddress": "Server Address",
        "status": "Status",
        "online": "Online",
        "offline": "Offline",
        "gpuResourceUsageTitle": "GPU Resource Usage",
        "gpuNumber": "GPU Number",
        "gpuID": "GPU ID",
        "totalMemory": "Total Memory",
        "freeMemory": "Free Memory",
        "gpuUtilization": "GPU Utilization",
        "machineName": "Server Name",
        "addOne": "Add One"
    },
    "flow": {
        "unsavedChangesConfirmation": "You have unsaved changes. Are you sure you want to leave?",
        "leave": "Leave",
        "leaveAndSave": "Leave and Save",
        "simplifyConfig": "Simplify Configuration",
        "simplify": "Simplify",
        "notifications": "Notifications",
        "exit": "Exit",
        "import": "Import",
        "export": "Export",
        "code": "Code",
        "searchComponent": "Search Component",
        "knowledgeBaseSelection": "Knowledge Base Selection",
        "searchKnowledgeBase": "Search Knowledge Base",
        "minimumParamSetDescription": "You can set the minimum parameter set required for the skill here",
        "paramList": "Parameter List",
        "saveConfig": "Save Configuration",
        "componentLabel": "Component",
        "aliasLabel": "Alias",
        "editAlias": "Edit Alias",
        "parameterLabel": "Parameter",
        "notification": "Notification",
        "noNewNotifications": "No new notifications",
        "skillName": "Skill Name",
        "nameTooLong": "Name is too long",
        "skillDescription": "Skill Description",
        "enterVarName": "Please enter a variable name.",
        "varNameExists": "Variable name is already in use",
        "text": "Text",
        "dropdown": "Dropdown",
        "maxLength": "Max Length",
        "options": "Options",
        "variableName": "Variable Name",
        "varOptionRequired": "options are required",
        "optionRepeated": "Option repeated",
        "incorrectIdFormatMessage": "Incorrect ID format (should be a 5-character combination of letters and numbers)",
        "idAlreadyExistsMessage": "This ID already exists"
    },
    "lib": {
        "enterLibraryName": "Enter Knowledge Base Name",
        "libraryNameLimit": "Knowledge Base name cannot exceed 30 characters",
        "selectModel": "Select a Model",
        "nameExists": "This name already exists",
        "descriptionLimit": "Knowledge Base description cannot exceed 200 characters",
        "createLibrary": "Create Knowledge Base",
        "libraryName": "Knowledge Base Name",
        "description": "Description",
        "model": "Model",
        "fileData": "File Data",
        "structuredData": "Structured Data",
        "libraryCollection": "Knowledge Base Collection",
        "createUser": "Create User",
        "details": "Details",
        "confirmDeleteLibrary": "Are you sure to delete this knowledge base?\nData cannot be restored after deletion, please confirm!",
        "fileList": "File List",
        "systemIntegration": "System Integration",
        "upload": "Upload",
        "fileName": "File Name",
        "status": "Status",
        "uploadTime": "Upload Time",
        "parseFailed": "Parse Failed",
        "parsing": "Parsing",
        "completed": "Completed",
        "confirmDeleteFile": "Confirm deletion of this file?",
        "fileUploadResult": "Out of the {{total}} files uploaded, {{failed}} failed to upload.",
        "myKnowledge": "My knowledge base",
        "createNew": "Create New",
        "folder": "Folder",
        "universalKnowledgeBase": "Universal Knowledge Base",
        "noKnowledgeBase": "We don't have a knowledge base yet, create one quickly.",
        "rootDirectory": "Root directory",
        "createOneKnowledge": "Create a knowledge base",
        "webSite": "Web site synchronization",
        "knowledgeType": "Knowledge base type",
        "knowledgeDescription": "Knowledge base can be built by importing files, webpage links, or manually entering them",
        "webSiteDescription": "Web site synchronization allows you to directly use a webpage link to build a knowledge base",
        "baseSet": "Basic Settings",
        "default": "Default",
        "localUpload": "Local upload",
        "aIGeneration": "AI generation",
        "name": "Name",
        "giveAName": "Give a name",
        "describe": "Please describe in one sentence",
        "useModel": "Using models",
        "cancel": "Cancel",
        "confirmCreation": "Confirm creation",
        "change": "Change",
        "rename": "Rename",
        "move": "Move",
        "createFolder": "Create folder",
        "createFolderDesc": "Please enter the folder name",
        "createDataset": "Create a manual dataset",
        "createDatasetDesc": "Manual datasets allow the creation of an empty container to load data",
        "synchronizationDesc": "Confirm starting data synchronization? \nOld data will be deleted and retrieved again. Please confirm!",
        "foldDesc": "This is a folder",
        "noFold": "We don't have a folder yet, create one quickly",
        "selectFold": "Please select a folder",
        "confirmDelete": "Confirm deletion",
        "dataset": "Dataset",
        "config": "Configure",
        "queueLen": "Queue length",
        "allKnowledge": "All datasets",
        "file": "File",
        "dataTotal": "Total data volume",
        "lastUpdate": "Last update time",
        "state": "State",
        "syncData": "Synchronize data",
        "manualDataset": "Manual dataset",
        "textDataset": "Text dataset",
        "tableDataset": "Table Dataset",
        "createImport": "Create/Import",
        "knowledge": "Knowledge base",
        "web": "Website",
        "knowledgeId": "Knowledge Base ID",
        "knowledgeImg": "Knowledge Base Avatar",
        "indexModel": "Index model",
        "dataUp": "Single data upper limit",
        "introduce": "Introduce"
    },
    "code": {
        "editPythonCodeDescription": "Edit your Python code here. This code snippet accepts module imports and a function definition. Make sure your function returns a string.",
        "editCode": "Edit Code",
        "codeReadyToRun": "Code ready to run",
        "functionError": "There is an error in your function",
        "importsError": "There is an error in your imports",
        "errorOccurred": "An error occurred, please retry",
        "codeError": "There is an issue with this code, please check the following",
        "checkAndSave": "Check & Save",
        "export": "Export",
        "exportToJSON": "Export Skill to JSON File",
        "keyInformationMissing": "You have some missing key information: ",
        "skillNameMissing": "Please enter a skill name",
        "useOwnAPIKeys": "Use your own API keys",
        "exportSkill": "Export Skill",
        "uploadFile": "Upload File",
        "clickOrDragHere": "Click or drag a file here to upload",
        "dropFileHere": "Drop file here to upload",
        "delimiter": "Delimiter (multiple with ;)",
        "splitLength": "Chunk Size",
        "smartSplit": "Smart Semantic Split",
        "manualSplit": "Manually Set Split",
        "delimiterPlaceholder": "Delimiter symbol",
        "splitSizePlaceholder": "Split Size",
        "complete": "Complete",
        "setSplitSize": "Please set the file split size",
        "selectFileToUpload": "Please select a file to upload first",
        "fileSizeExceedsLimit": "File size cannot exceed 50MB,removed",
        "file": "File",
        "sizeExceedsLimit": "Size exceeds 50MB",
        "editDictionary": "Edit Dictionary",
        "exportCodeDialogTip": "Generate code to integrate the workflow into an external application (please make sure to build the skill before opening this page).",
        "chunkOverlap": "Chunk Overlap"
    },
    "report": {
        "reportTemplate": "Report Template",
        "reportDescription": "Report generation description...",
        "newButton": "New",
        "importButton": "Import",
        "start": "start",
        "formSettings": "Form Settings",
        "requiredLabel": "Required",
        "isRequired": "is Required",
        "fileRequired": "The current file is empty",
        "selectComponent": "Select a component",
        "varLength": "Length cannot exceed"
    },
    "status": {
        "1004": "This skill has been deleted",
        "1008": "The current skill is not online, and direct conversation is not possible.",
        "1005": ""
    },
    "all": "All",
    "finetune": {
        "modelName": "Model Name",
        "rtService": "RT Services",
        "createTime": "Creation Time",
        "noData": "No Data",
        "selectModel": "Select model for details.",
        "trainingLogs": "Training Logs",
        "evaluationReport": "Evaluation Report",
        "all": "All",
        "successful": "Succeed",
        "inProgress": "Ongoing",
        "failedAborted": "Failed/Aborted",
        "createTrainingTask": "Create training tasks",
        "rtServiceManagement": "RT service management",
        "confirmCancelPublish": "This model is currently online, whether it is still unpublished?",
        "confirmDeleteModel": "Are you sure you want to delete the model {{name}}?",
        "confirmDeleteOnlineModel": "This model has been online, do you want to delete it?",
        "confirmDeletePublishedModel": "The model has been published, confirm that you want to delete the model {{name}}?",
        "confirmStopTraining": "Confirm stop this training?",
        "trainingInProgress": "Training",
        "trainingFailed": "Training failed",
        "taskAborted": "Task aborted",
        "trainingSuccess": "Training succeed",
        "publishSuccess": "Publish succeed",
        "taskId": "Task Id",
        "baseModel": "Base Model",
        "runtime": "Runtime",
        "formatTime": "hh hours mm minutes ss seconds",
        "creator": "Creator",
        "dataset": "Dataset",
        "cancelPublish": "Cancel publish",
        "publish": "Publish",
        "stop": "Stop",
        "uploadDataset": "Upload Personal Dataset",
        "downloadSampleFile": "Download sample file",
        "customSampleSize": "Custom sample count (optional)",
        "customSampleSizeTooltip1": "This is an optional item:",
        "customSampleSizeTooltip2": "If this option is not selected, all samples of the selected personal data set and the pre-set data set will participate in training;",
        "customSampleSizeTooltip3": "If this option is selected, the user can freely specify the number of samples used for training in different data sets. If the input box is empty or the number entered is greater than the total number of samples in the data set, all samples in the data set will be used for training.",
        "presetDatasets": "Preset Datasets",
        "download": "Download",
        "sampleSize": "Sample Size",
        "userDatasets": "Personal Dataset",
        "gpuDesc": "The number of the graphics card used for training, and multiple cards are separated by commas, such as 0, 1, 2, and 3",
        "valRatioDesc": "Verification set ratio: if the value is greater than 0, loss will be calculated on the validation set after each epoch",
        "batchSizeDesc": "Batch size indicates the number of samples used in each training iteration. A larger batch size can accelerate training, but may lead to excessive memory usage;",
        "learningRateDesc": "A learning rate (learning rate) is a hyperparameter used to update weights during the process of gradient descent; if it is too high, the model will be difficult to converge, while if it is too low, the model will converge slowly.",
        "numEpochsDesc": "The number of iterations (epochs) controls the number of iterations in the training process; you can judge whether the model is converged according to the loss curve, if the loss is still decreasing without being stable, you can increase the epoch.",
        "maxSeqLenDesc": "Specify the maximum sequence length. The default value is 8192, i.e. the input and output lengths cannot exceed 8192 or they will be truncated;",
        "cpuLoadDesc": "Whether to load some parameters and optimizers into the CPU (the default is not enabled) can be turned on after opening it will occupy memory detailed data can refer to resource consumption related documentation",
        "selectRTService": "Please select RT service",
        "selectBaseModel": "Please select the base model",
        "enterModelName": "The model name contains at least one letter and is composed of numbers, letters, and underscores.",
        "rtServiceTooltip": "Select the RT service where the target benchmark model is located; Finetune will also be deployed in the same RT service after training completes.",
        "finetuneModelName": "The name of the Finetune model",
        "trainingMethod": "Training method",
        "fullFineTune": "Full-scale fine tuning",
        "freeze": "freeze",
        "lora": "lora",
        "parameterConfiguration": "Parameter Configuration",
        "parameterConfigurationTooltip": "Parameter configuration suggestions and experimental reference data are available in the product document.",
        "parameter": "Parameter",
        "quantity": "Quantity",
        "description": "Description",
        "gpuResourceUsage": "GPU resource usage"
    },
    "confirmButton": "Confirm",
    "add": "Add",
    "back": "Back",
    "create": "Create",
    "delete": "Delete",
    "createTime": "Creation Time",
    "updateTime": "Update Time",
    "success": "Saved",
    "edit": "Edit",
    "enable": "Enable",
    "disable": "Disable",
    "close": "Close",
    "cancel": "Cancel",
    "save": "Save",
    "submit": "Submit",
    "operations": "Operations",
    "previousPage": "Previous Page",
    "nextPage": "Next Page",
    "formatError": "Format Error",
    "agents": {
        "AgentInitializer":{
            "display_name": "AgentInitializer",
            "description": "Construct a zero shot agent from an LLM and tools.",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "LLM"
                },
                "memory": {
                    "display_name": "memory"
                },
                "tools": {
                    "display_name": "tools"
                },
                "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_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "LLM"
                },
                "path": {
                    "display_name": "path"
                },
                "format_instructions": {
                    "display_name": "format_instructions",
                    "value": "Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action Observation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question"
                },
                "input_variables": {
                    "display_name": "input_variables",
                    "value": [
                        "df_head",
                        "input",
                        "agent_scratchpad"
                    ]
                },
                "prefix": {
                    "display_name": "prefix",
                    "value": "\nYou are working with a pandas dataframe in Python. The name of the dataframe is `df`.\nYou should use the tools below to answer the question posed of you:"
                },
                "suffix": {
                    "display_name": "suffix",
                    "value": "\nThis is the result of `print(df.head())`:\n{df_head}\n\nBegin!\nQuestion: {input}\n{agent_scratchpad}"
                }
            }
        },
        "ChatglmFunctionsAgent": {
            "display_name": "ChatglmFunctionsAgent",
            "description": "Construct an agent from an LLM and tools.",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "LLM"
                },
                "tools": {
                    "display_name": "tools"
                }
            }
        },
        "JsonAgent": {
            "display_name": "JsonAgent",
            "description": "Construct a json agent from an LLM and tools.",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "LLM"
                },
                "toolkit": {
                    "display_name": "toolkit"
                }
            }
        },
        "LLMFunctionsAgent": {
            "display_name": "LLMFunctionsAgent",
            "description": "Construct an agent from an LLM and tools.",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "LLM"
                },
                "tools": {
                    "display_name": "tools"
                }
            }
        },
        "SQLAgent": {
            "display_name": "SQLAgent",
            "description": "Construct an SQL agent from an LLM and tools.",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "LLM"
                },
                "database_uri": {
                    "display_name": "database_uri"
                },
                "format_instructions": {
                    "display_name": "format_instructions",
                    "value": "Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question"
                },
                "input_variables": {
                    "display_name": "input_variables",
                    "value": [
                        "input",
                        "agent_scratchpad"
                    ]
                },
                "prefix": {
                    "display_name": "prefix",
                    "value": "You are an agent designed to interact with a SQL database.\nGiven an input question, create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.\nUnless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.\nYou can order the results by a relevant column to return the most interesting examples in the database.\nNever query for all the columns from a specific table, only ask for the relevant columns given the question.\nYou have access to tools for interacting with the database.\nOnly use the below tools. Only use the information returned by the below tools to construct your final answer.\nYou MUST double check your query before executing it. If you get an error while executing a query, rewrite the query and try again.\n\nDO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.\n\nIf the question does not seem related to the database, just return 'I don't know' as the answer."
                },
                "suffix": {
                    "display_name": "suffix",
                    "value": "Begin!\n\nQuestion: {input}\nThought: I should look at the tables in the database to see what I can query.  Then I should query the schema of the most relevant tables.\n{agent_scratchpad}"
                }
            }
        },
        "VectorStoreAgent": {
            "display_name": "VectorStoreAgent",
            "description": "Construct an agent from a Vector Store.",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "LLM"
                },
                "vectorstoreinfo": {
                    "display_name": "Vector Store Info"
                }
            }
        },
        "VectorStoreRouterAgent": {
            "display_name": "VectorStoreRouterAgent",
            "description": "Construct an agent from a Vector Store Router.",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "LLM"
                },
                "vectorstoreroutertoolkit": {
                    "display_name": "Vector Store Router Toolkit"
                }
            }
        },
        "ZeroShotAgent": {
            "display_name": "ZeroShotAgent",
            "description": "Construct an agent from an LLM and tools.",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "LLM"
                },
                "tools": {
                    "display_name": "tools"
                },
                "format_instructions": {
                    "display_name": "format_instructions",
                    "value": "Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question"
                },
                "input_variables": {
                    "display_name": "input_variables"
                },
                "prefix": {
                    "display_name": "prefix",
                    "value": "Answer the following questions as best you can. You have access to the following tools:"
                },
                "suffix": {
                    "display_name": "suffix",
                    "value": "Begin!\n\nQuestion: {input}\nThought:{agent_scratchpad}"
                }
            },
            "output_types":[
                "ZeroShotAgent"
            ]
        }
    },
    "autogen_roles": {
        "AutoGenAssistant": {
            "display_name": "AutoGenAssistant",
            "description": "Assistant agent, designed to solve a task with LLM.",
            "description_url": "",
            "template": {
                "model_name": {
                    "display_name": "model_name"
                },
                "name": {
                    "display_name": "name"
                },
                "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": "system_message"
                },
                "temperature": {
                    "display_name": "temperature"
                }
            },
            "output_types":[
                "AutoGenAssistant"
            ]
        },
        "AutoGenCoder": {
            "display_name": "AutoGenCoder",
            "description": "A proxy agent for the coder, that can execute code to the other agents.",
            "description_url": "",
            "template": {
                "name": {
                    "display_name": "name"
                },
                "system_message": {
                    "display_name": "system_message"
                }
            },
            "output_types":[
                "AutoGenCoder"
            ]
        },
        "AutoGenCustomRole": {
            "display_name": "AutoGenCustomRole",
            "description": "Custom agent that can use langchain agent and chain.",
            "description_url": "",
            "template": {
                "func": {
                    "display_name": "func"
                },
                "name": {
                    "display_name": "name"
                },
                "system_message": {
                    "display_name": "system_message"
                }
            },
            "output_types":[
                "AutoGenCustomRole"
            ]
        },
        "AutoGenGroupChatManager": {
            "display_name": "AutoGenGroupChatManager",
            "description": "A chat manager agent that can manage a group chat of multiple agents.",
            "description_url": "",
            "template": {
                "agents": {
                    "display_name": "agents"
                },
                "max_round": {
                    "display_name": "max_round"
                },
                "model_name": {
                    "display_name": "model_name"
                },
                "name": {
                    "display_name": "name",
                    "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": "system_message"
                },
                "temperature": {
                    "display_name": "temperature"
                }
            },
            "output_types":[
                "AutoGenGroupChatManager"
            ]
        },
        "AutoGenUser": {
            "display_name": "AutoGenUser",
            "description": "A proxy agent for the user, that can provide feedback to the other agents.",
            "description_url": "",
            "template": {
                "human_input_mode": {
                    "display_name": "human_input_mode"
                },
                "max_consecutive_auto_reply": {
                    "display_name": "max_consecutive_auto_reply"
                },
                "name": {
                    "display_name": "name",
                    "value": "chat_manage"
                },
                "system_message": {
                    "display_name": "system_message"
                }
            },
            "output_types":[
                "AutoGenUser"
            ]
        }
    },
    "chains": {
        "APIChain": {
            "display_name": "APIChain",
            "description": "Load chain from just an LLM and the api docs.",
            "description_url": "",
            "template": {
                "api_response_prompt": {
                    "display_name": "api_response_prompt"
                },
                "api_url_prompt": {
                    "display_name": "api_url_prompt"
                },
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "llm"
                },
                "api_docs": {
                    "display_name": "api_docs"
                },
                "headers": {
                    "display_name": "headers"
                }
            },
            "output_types":[
                "APIChain"
            ]
        },
        "AutoGenChain": {
            "display_name": "AutoGenChain",
            "description": "Chain that print the loader output.",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "memory": {
                    "display_name": "memory"
                },
                "recipient": {
                    "display_name": "AutogenRole"
                },
                "user_proxy_agent": {
                    "display_name": "user_proxy_agent"
                }
            },
            "output_types":[
                "AutoGenChain"
            ]
        },
        "CombineDocsChain": {
            "display_name": "CombineDocsChain",
            "description": "Load question answering chain.",
            "description_url": "",
            "template": {
                "document_prompt": {
                    "display_name": "document_prompt"
                },
                "llm": {
                    "display_name": "LLM"
                },
                "prompt": {
                    "display_name": "prompt"
                },
                "chain_type": {
                    "display_name": "chain_type",
                    "options": [
                        "stuff",
                        "map_reduce",
                        "map_rerank",
                        "refine"
                    ]
                },
                "token_max": {
                    "display_name": "token_max"
                }
            },
            "output_types":[
                "CombineDocsChain"
            ]
        },
        "ConversationChain": {
            "display_name": "ConversationChain",
            "description": "Chain to have a conversation and load context from memory.",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "LLM"
                },
                "memory": {
                    "display_name": "memory"
                }
            },
            "output_types":[
                "ConversationChain"
            ]
        },
        "ConversationalRetrievalChain": {
            "display_name": "ConversationalRetrievalChain",
            "description": "Convenience method to load chain from LLM and retriever.",
            "description_url": "",
            "template": {
                "combine_docs_chain_kwargs": {
                    "display_name": "prompt"
                },
                "condense_question_prompt": {
                    "display_name": "condense_question_prompt"
                },
                "document_prompt": {
                    "display_name": "document_prompt"
                },
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "LLM"
                },
                "memory": {
                    "display_name": "memory"
                },
                "retriever": {
                    "display_name": "retriever"
                },
                "chain_type": {
                    "display_name": "chain_type",
                    "options": [
                        "stuff",
                        "map_reduce",
                        "map_rerank",
                        "refine"
                    ]
                }
            },
            "output_types":[
                "ConversationalRetrievalChain"
            ]
        },
        "DalleGeneratorChain":{
            "display_name": "DalleGeneratorChain",
            "description": "Implementation of dall-e generate images",
            "description_url": "https://lxm3ki8hexz.feishu.cn/wiki/VPcowiAREijlXfksRaScA40in6c",
        },
        "LLMChain": {
            "display_name": "LLMChain",
            "description": "Chain to run queries against LLMs.",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "LLM"
                },
                "memory": {
                    "display_name": "memory"
                },
                "prompt": {
                    "display_name": "prompt"
                }
            },
            "output_types":[
                "LLMChain"
            ]
        },
        "LLMCheckerChain": {
            "display_name": "LLMCheckerChain",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "LLM"
                }
            },
            "output_types":[
                "LLMCheckerChain"
            ]
        },
        "LLMMathChain": {
            "display_name": "LLMMathChain",
            "description": "Chain that interprets a prompt and executes python code to do math.",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "LLM"
                },
                "llm_chain": {
                    "display_name": "llm_chain"
                },
                "memory": {
                    "display_name": "memory"
                }
            },
            "output_types":[
                "LLMMathChain"
            ]
        },
        "LLMRouterChain": {
            "display_name": "LLMRouterChain",
            "description": "Convenience constructor.",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "LLM"
                },
                "llm_chain": {
                    "display_name": "llm_chain"
                },
                "prompt": {
                    "display_name": "prompt"
                }
            },
            "output_types":[
                "LLMRouterChain"
            ]
        },
        "LoaderOutputChain": {
            "display_name": "LoaderOutputChain",
            "description": "Chain that print the loader output.",
            "description_url": "",
            "template": {
                "documents": {
                    "display_name": "documents"
                },
                "input_node": {
                    "display_name": "Preset Question"
                },
                "memory": {
                    "display_name": "memory"
                }
            },
            "output_types":[
                "LoaderOutputChain"
            ]
        },
        "MidJourneyPromptChain": {
            "display_name": "MidJourneyPromptChain",
            "description": "MidJourneyPromptChain is a chain you can use to generate new MidJourney prompts.",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "LLM"
                },
                "memory": {
                    "display_name": "memory"
                }
            },
            "output_types":[
                "MidJourneyPromptChain"
            ]
        },
        "MultiPromptChain": {
            "display_name": "MultiPromptChain",
            "description": "A multi-route chain that uses an LLM router chain to choose amongst prompts.",
            "description_url": "",
            "template": {
                "LLMChains": {
                    "display_name": "LLMChains"
                },
                "default_chain": {
                    "display_name": "default_chain"
                },
                "input_node": {
                    "display_name": "Preset Question"
                },
                "memory": {
                    "display_name": "memory"
                },
                "router_chain": {
                    "display_name": "router_chain"
                },
                "destination_chain_name": {
                    "display_name": "destination_chain_name"
                }
            },
            "output_types":[
                "MultiPromptChain"
            ]
        },
        "MultiRuleChain": {
            "display_name": "MultiRuleChain",
            "description_url": "",
            "template": {
                "LLMChains": {
                    "display_name": "LLMChains"
                },
                "default_chain": {
                    "display_name": "default_chain"
                },
                "input_node": {
                    "display_name": "Preset Question"
                },
                "memory": {
                    "display_name": "memory"
                },
                "router_chain": {
                    "display_name": "router_chain"
                },
                "destination_chain_name": {
                    "display_name": "destination_chain_name"
                },
                "output_variables": {
                    "display_name": "output_variables"
                }
            },
            "output_types":[
                "MultiRuleChain"
            ]
        },
        "RetrievalChain": {
            "display_name": "RetrievalChain",
            "description": "Chain to use to combine the documents.",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "memory": {
                    "display_name": "memory"
                },
                "retriever": {
                    "display_name": "retriever"
                }
            },
            "output_types":[
                "RetrievalChain"
            ]
        },
        "RetrievalQA": {
            "display_name": "RetrievalQA",
            "description": "Chain for question-answering against an index.",
            "description_url": "",
            "template": {
                "memory": {
                    "display_name": "memory"
                },
                "retriever": {
                    "display_name": "retriever"
                },
                "input_node": {
                    "display_name": "Preset Question"
                },
                "combine_documents_chain": {
                    "display_name": "combine_documents_chain"
                }
            },
            "output_types":[
                "RetrievalQA"
            ]
        },
        "RetrievalQAWithSourcesChain": {
            "display_name": "RetrievalQAWithSourcesChain",
            "description": "Question-answering with sources over an index.",
            "description_url": "",
            "template": {
                "combine_documents_chain": {
                    "display_name": "combine_documents_chain"
                },
                "input_node": {
                    "display_name": "Preset Question"
                },
                "memory": {
                    "display_name": "memory"
                },
                "retriever": {
                    "display_name": "retriever"
                }
            },
            "output_types":[
                "RetrievalQAWithSourcesChain"
            ]
        },
        "RuleBasedRouter": {
            "display_name": "RuleBasedRouter",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "memory": {
                    "display_name": "memory"
                },
                "rule_function": {
                    "display_name": "rule_function"
                },
                "input_variables": {
                    "display_name": "input_variables"
                }
            },
            "output_types":[
                "RuleBasedRouter"
            ]
        },
        "SQLDatabaseChain": {
            "display_name": "SQLDatabaseChain",
            "description_url": "",
            "template": {
                "db": {
                    "display_name": "db"
                },
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "llm"
                },
                "prompt": {
                    "display_name": "prompt"
                }
            },
            "output_types":[
                "SQLDatabaseChain"
            ]
        },
        "SequentialChain": {
            "display_name": "SequentialChain",
            "description": "Chain where the outputs of one chain feed directly into next.",
            "description_url": "",
            "template": {
                "chains": {
                    "display_name": "chains"
                },
                "input_node": {
                    "display_name": "Preset Question"
                },
                "memory": {
                    "display_name": "memory"
                },
                "chain_order": {
                    "display_name": "chain_order"
                },
                "input_variables": {
                    "display_name": "input_variables"
                },
                "output_variables": {
                    "display_name": "output_variables"
                }
            },
            "output_types":[
                "SequentialChain"
            ]
        },
        "SeriesCharacterChain": {
            "display_name": "SeriesCharacterChain",
            "description": "SeriesCharacterChain is a chain you can use to have a conversation with a character from a series.",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "LLM"
                },
                "character": {
                    "display_name": "character"
                },
                "series": {
                    "display_name": "series"
                }
            },
            "output_types":[
                "SeriesCharacterChain"
            ]
        },
        "SimpleSequentialChain": {
            "display_name": "SimpleSequentialChain",
            "description": "Simple chain where the outputs of one step feed directly into next.",
            "description_url": "",
            "template": {
                "chains": {
                    "display_name": "chains"
                },
                "input_node": {
                    "display_name": "Preset Question"
                },
                "memory": {
                    "display_name": "memory"
                }
            },
            "output_types":[
                "SimpleSequentialChain"
            ]
        },
        "TimeTravelGuideChain": {
            "display_name": "TimeTravelGuideChain",
            "description": "Time travel guide chain.",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "llm": {
                    "display_name": "LLM"
                },
                "memory": {
                    "display_name": "memory"
                }
            },
            "output_types":[
                "TimeTravelGuideChain"
            ]
        },
        "TransformChain": {
            "display_name": "TransformChain",
            "description": "Chain transform chain output.",
            "description_url": "",
            "template": {
                "input_node": {
                    "display_name": "Preset Question"
                },
                "memory": {
                    "display_name": "memory"
                },
                "transform": {
                    "display_name": "transform"
                },
                "input_variables": {
                    "display_name": "input_variables"
                },
                "output_variables": {
                    "display_name": "output_variables"
                }
            },
            "output_types":[
                "TransformChain"
            ]
        }
    },
    "documentloaders": {
        "AZLyricsLoader": {
            "display_name": "AZLyricsLoader",
            "description": "Loads AZLyrics webpages.",
            "description_url": "",
            "template": {
                "metadata": {
                    "display_name": "metadata"
                },
                "web_path": {
                    "display_name": "web_path"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "AirbyteJSONLoader": {
            "display_name": "AirbyteJSONLoader",
            "description": "Loads local airbyte json files.",
            "description_url": "",
            "template": {
                "file_path": {
                    "display_name": "file_path"
                },
                "metadata": {
                    "display_name": "metadata"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "BSHTMLLoader": {
            "display_name": "BSHTMLLoader",
            "description": "Loader that uses beautiful soup to parse HTML files.",
            "description_url": "",
            "template": {
                "file_path": {
                    "display_name": "file_path"
                },
                "metadata": {
                    "display_name": "metadata"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "CSVLoader": {
            "display_name": "CSVLoader",
            "description": "Loads a CSV file into a list of documents.",
            "description_url": "",
            "template": {
                "file_path": {
                    "display_name": "file_path"
                },
                "metadata": {
                    "display_name": "metadata"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "CoNLLULoader": {
            "display_name": "CoNLLULoader",
            "description": "Load CoNLL-U files.",
            "description_url": "",
            "template": {
                "file_path": {
                    "display_name": "file_path"
                },
                "metadata": {
                    "display_name": "metadata"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "CollegeConfidentialLoader": {
            "display_name": "CollegeConfidentialLoader",
            "description": "Loads College Confidential webpages.",
            "description_url": "",
            "template": {
                "metadata": {
                    "display_name": "metadata"
                },
                "web_path": {
                    "display_name": "web_path"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "CustomKVLoader": {
            "display_name": "CustomKVLoader",
            "description": "Extract key-value from pdf or image.",
            "description_url": "",
            "template": {
                "file_path": {
                    "display_name": "file_path"
                },
                "elem_server_id": {
                    "display_name": "elem_server_id"
                },
                "elm_api_base_url": {
                    "display_name": "elm_api_base_url"
                },
                "elm_api_key": {
                    "display_name": "elm_api_key"
                },
                "metadata": {
                    "display_name": "Metadata"
                },
                "schemas": {
                    "display_name": "schemas"
                },
                "task_type": {
                    "display_name": "task_type"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "DirectoryLoader": {
            "display_name": "DirectoryLoader",
            "description": "Load documents from a directory.",
            "description_url": "",
            "template": {
                "glob": {
                    "display_name": "glob"
                },
                "metadata": {
                    "display_name": "Metadata"
                },
                "path": {
                    "display_name": "Local directory"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "ElemUnstructuredLoaderV0": {
            "display_name": "ElemUnstructuredLoaderV0",
            "description": "The appropriate parser is automatically selected based on the file format and OCR is supported",
            "description_url": "",
            "template": {
                "file_path": {
                    "display_name": "file_path"
                },
                "metadata": {
                    "display_name": "Metadata"
                },
                "unstructured_api_url": {
                    "display_name": "unstructured_api_url"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "EverNoteLoader": {
            "display_name": "EverNoteLoader",
            "description": "EverNote Loader.",
            "description_url": "",
            "template": {
                "file_path": {
                    "display_name": "file_path"
                },
                "metadata": {
                    "display_name": "metadata"
                }
            },
            "base_classes": [
                "Document"
            ]
        },
        "FacebookChatLoader": {
            "display_name": "FacebookChatLoader",
            "description": "Loads Facebook messages json directory dump.",
            "description_url": "",
            "template": {
                "file_path": {
                    "display_name": "file_path"
                },
                "metadata": {
                    "display_name": "metadata"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "GitLoader": {
            "display_name": "GitLoader",
            "description": "Loads files from a Git repository into a list of documents.",
            "description_url": "",
            "template": {
                "branch": {
                    "display_name": "Branch"
                },
                "clone_url": {
                    "display_name": "Clone URL"
                },
                "file_filter": {
                    "display_name": "File extensions (comma-separated)"
                },
                "metadata": {
                    "display_name": "Metadata"
                },
                "repo_path": {
                    "display_name": "Path to repository"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "GitbookLoader": {
            "display_name": "GitbookLoader",
            "description": "Load GitBook data.",
            "description_url": "",
            "template": {
                "metadata": {
                    "display_name": "metadata"
                },
                "web_page": {
                    "display_name": "Web Page"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "GutenbergLoader": {
            "display_name": "GutenbergLoader",
            "description": "Loader that uses urllib to load .txt web files.",
            "description_url": "",
            "template": {
                "metadata": {
                    "display_name": "metadata"
                },
                "web_page": {
                    "display_name": "Web Page"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "HNLoader": {
            "display_name": "HNLoader",
            "description": "Load Hacker News data from either main page results or the comments page.",
            "description_url": "",
            "template": {
                "metadata": {
                    "display_name": "metadata"
                },
                "web_page": {
                    "display_name": "Web Page"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "IFixitLoader": {
            "display_name": "IFixitLoader",
            "description": "Load iFixit repair guides, device wikis and answers.",
            "description_url": "",
            "template": {
                "metadata": {
                    "display_name": "metadata"
                },
                "web_page": {
                    "display_name": "Web Page"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "IMSDbLoader": {
            "display_name": "IMSDbLoader",
            "description": "Loads IMSDb webpages.",
            "description_url": "",
            "template": {
                "metadata": {
                    "display_name": "metadata"
                },
                "web_page": {
                    "display_name": "Web Page"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "NotionDirectoryLoader": {
            "display_name": "NotionDirectoryLoader",
            "description": "Loads Notion directory dump.",
            "description_url": "",
            "template": {
                "metadata": {
                    "display_name": "Metadata"
                },
                "path": {
                    "display_name": "Local directory"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "PDFWithSemanticLoader": {
            "display_name": "PDFWithSemanticLoader",
            "description": "Loads a PDF with pypdf and chunks at character level.",
            "description_url": "",
            "template": {
                "file_path": {
                    "display_name": "file_path"
                },
                "layout_api_key": {
                    "display_name": "layout_api_key"
                },
                "layout_api_url": {
                    "display_name": "layout_api_url"
                },
                "metadata": {
                    "display_name": "Metadata"
                }
                
            },
            "output_types": [
                "Document"
            ]
        },
        "PyPDFDirectoryLoader": {
            "display_name": "PyPDFDirectoryLoader",
            "description": "Loads a directory with PDF files with pypdf and chunks at character level.",
            "description_url": "",
            "template": {
                "metadata": {
                    "display_name": "Metadata"
                },
                "path": {
                    "display_name": "Local directory"
                }
                
            },
            "output_types": [
                "Document"
            ]
        },
        "PyPDFLoader": {
            "display_name": "PyPDFLoader",
            "description": "Loads a PDF with pypdf and chunks at character level.",
            "description_url": "",
            "template": {
                "file_path": {
                    "display_name": "file_path"
                },
                "metadata": {
                    "display_name": "Metadata"
                }            
            },
            "output_types": [
                "Document"
            ]
        },
        "ReadTheDocsLoader": {
            "display_name": "ReadTheDocsLoader",
            "description": "Loads ReadTheDocs documentation directory dump.",
            "description_url": "",
            "template": {
                "metadata": {
                    "display_name": "Metadata"
                },
                "path": {
                    "display_name": "Local directory"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "SRTLoader": {
            "display_name": "SRTLoader",
            "description": "Loader for .srt (subtitle) files.",
            "description_url": "",
            "template": {
                "file_path": {
                    "display_name": "file_path"
                },
                "metadata": {
                    "display_name": "Metadata"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "SlackDirectoryLoader": {
            "display_name": "SlackDirectoryLoader",
            "description": "Loads documents from a Slack directory dump.",
            "description_url": "",
            "template": {
                "file_path": {
                    "display_name": "file_path"
                },
                "metadata": {
                    "display_name": "Metadata"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "TextLoader": {
            "display_name": "TextLoader",
            "description": "Load text files.",
            "description_url": "",
            "template": {
                "file_path": {
                    "display_name": "file_path"
                },
                "metadata": {
                    "display_name": "Metadata"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "UniversalKVLoader": {
            "display_name": "UniversalKVLoader",
            "description": "Extract key-value from pdf or image.",
            "description_url": "",
            "template": {
                "file_path": {
                    "display_name": "file_path"
                },
                "ellm_model_url": {
                    "display_name": "ellm_model_url"
                },
                "max_pages": {
                    "display_name": "max_pages"
                },
                "metadata": {
                    "display_name": "Metadata"
                },
                "schema": {
                    "display_name": "schema"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "WebBaseLoader": {
            "display_name": "WebBaseLoader",
            "description": "Loader that uses urllib and beautiful soup to load webpages.",
            "description_url": "",
            "template": {
                "metadata": {
                    "display_name": "Metadata"
                },
                "web_path": {
                    "display_name": "Web Page"
                }
            },
            "output_types": [
                "Document"
            ]
        }
    },
    "embeddings": {
        "CohereEmbeddings": {
            "display_name": "CohereEmbeddings",
            "description": "Wrapper around Cohere embedding models.",
            "description_url": "",
            "template": {
                "cohere_api_key": {
                    "display_name": "cohere_api_key"
                },
                "model": {
                    "display_name": "model"
                }
            }
        },
        "HostEmbeddings": {
            "display_name": "HostEmbeddings",
            "description": "host embedding models.",
            "description_url": "",
            "template": {
                "host_base_url": {
                    "display_name": "host_base_url"
                },
                "model": {
                    "display_name": "model"
                }
            },
            "output_types": [
                "HostEmbeddings"
            ]
        },
        "HuggingFaceEmbeddings": {
            "display_name": "HuggingFaceEmbeddings",
            "description": "Wrapper around sentence_transformers embedding models.",
            "description_url": ""
        },
        "OpenAIEmbeddings": {
            "display_name": "OpenAIEmbeddings",
            "description": "Wrapper around OpenAI embedding models.",
            "description_url": "",
            "template": {
                "model": {
                    "display_name": "model"
                },
                "openai_api_key": {
                    "display_name": "OpenAI API Key"
                },
                "tiktoken_model_name": {
                    "display_name": "tiktoken_model_name"
                }
            }
        },
        "OpenAIProxyEmbedding": {
            "display_name": "OpenAIProxyEmbedding",
            "description": " 使用自建的embedding服务使用openai进行embed ",
            "description_url": ""
        },
        "WenxinEmbeddings": {
            "display_name": "WenxinEmbeddings",
            "description": "Wenxin embedding models.",
            "description_url": "",
            "template": {
                "model": {
                    "display_name": "model"
                },
                "wenxin_api_key": {
                    "display_name": "wenxin_api_key"
                }
            }
        }
    },
    "input_output": {
        "InputFileNode": {
            "display_name": "InputFileNode",
            "description": "输入节点，用来自动对接输入",
            "description_url": "",
            "template": {
                "file_path": {
                    "display_name": "file_path"
                },
                "file_type": {
                    "display_name": "Name"
                }
            },
            "output_types": [
                "InputFileNode"
            ]
        },
        "InputNode": {
            "display_name": "InputNode",
            "description": "输入节点，用来自动对接输入",
            "description_url": "",
            "template": {
                "input": {
                    "display_name": "输入内容"
                }
            },
            "output_types": [
                "InputNode"
            ]
        },
        "Report": {
            "display_name": "Report",
            "description_url": "",
            "template": {
                "chains": {
                    "display_name": "functions"
                },
                "variables": {
                    "display_name": "variables"
                },
                "report_name": {
                    "display_name": "Report Name"
                }
            },
            "output_types": [
                "Report"
            ]
        },
        "VariableNode": {
            "display_name": "VariableNode",
            "description": "用来设置变量",
            "description_url": "",
            "template": {
                "variables": {
                    "display_name": "variables"
                }
            },
            "output_types": [
                "VariableNode"
            ]
        }
    },
    "llms": {
        "Anthropic": {
            "display_name": "Anthropic",
            "description": "Wrapper around Anthropic's large language models.",
            "description_url": "",
            "template": {
                "anthropic_api_key": {
                    "display_name": "anthropic_api_key"
                },
                "anthropic_api_url": {
                    "display_name": "anthropic_api_url"
                },
                "temperature": {
                    "display_name": "temperature"
                }
            }
        },
        "AzureChatOpenAI": {
            "display_name": "AzureChatOpenAI",
            "description": "Wrapper around Azure OpenAI Chat Completion API. To use this class you",
            "description_url": "",
            "template": {
                "model_name": {
                    "display_name": "model_name"
                },
                "openai_proxy": {
                    "display_name": "OpenAI Proxy"
                },
                "temperature": {
                    "display_name": "temperature"
                }
            }
        },
        "CTransformers": {
            "display_name": "CTransformers",
            "description": "Wrapper around the C Transformers LLM interface.",
            "description_url": "",
            "template": {
                "model": {
                    "display_name": "model"
                },
                "model_file": {
                    "display_name": "model_file"
                },
                "model_type": {
                    "display_name": "model_type"
                }
            }
        },
        "ChatAnthropic": {
            "display_name": "ChatAnthropic",
            "description": "Wrapper around Anthropic's large language model.",
            "description_url": "",
            "template": {
                "anthropic_api_key": {
                    "display_name": "anthropic_api_key"
                },
                "anthropic_api_url": {
                    "display_name": "anthropic_api_url"
                },
                "temperature": {
                    "display_name": "temperature"
                }
            }
        },
        "ChatMinimaxAI": {
            "display_name": "ChatMinimaxAI",
            "description": "Wrapper around proxy Chat large language models.",
            "description_url": "",
            "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": "temperature"
                }
            }
        },
        "ChatOpenAI": {
            "display_name": "ChatOpenAI",
            "description": "Wrapper around OpenAI Chat large language models.",
            "description_url": "",
            "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 Proxy"
                },
                "temperature": {
                    "display_name": "temperature"
                }
            }
        },
        "ChatQWen": {
            "display_name": "ChatQWen",
            "description": "Wrapper around proxy Chat large language models.",
            "description_url": "",
            "template": {
                "api_key": {
                    "display_name": "api_key"
                },
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "temperature"
                }
            }
        },
        "ChatWenxin": {
            "display_name": "ChatWenxin",
            "description": "Wrapper around proxy Chat large language models.",
            "description_url": "",
            "template": {
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "temperature"
                },
                "wenxin_api_key": {
                    "display_name": "wenxin_api_key"
                },
                "wenxin_secret_key": {
                    "display_name": "wenxin_secret_key"
                }
            }
        },
        "ChatXunfeiAI": {
            "display_name": "ChatXunfeiAI",
            "description": "Wrapper around proxy Chat large language models.",
            "description_url": "",
            "template": {
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "temperature"
                },
                "xunfeiai_api_key": {
                    "display_name": "xunfeiai_api_key"
                },
                "xunfeiai_api_secret": {
                    "display_name": "xunfeiai_api_secret"
                },
                "xunfeiai_appid": {
                    "display_name": "xunfeiai_appid"
                }
            }
        },
        "ChatZhipuAI": {
            "display_name": "ChatZhipuAI",
            "description": "Wrapper around ZhipuAI Chat large language models.",
            "description_url": "",
            "template": {
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "temperature"
                },
                "zhipuai_api_key": {
                    "display_name": "zhipuai_api_key"
                }
            }
        },
        "Cohere": {
            "display_name": "Cohere",
            "description": "Wrapper around Cohere large language models.",
            "description_url": "",
            "template": {
                "cohere_api_key": {
                    "display_name": "cohere_api_key"
                },
                "temperature": {
                    "display_name": "temperature"
                }
            }
        },
        "CustomLLMChat": {
            "display_name": "CustomLLMChat",
            "description_url": "",
            "template": {
                "host_base_url": {
                    "display_name": "host_base_url"
                },
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "temperature"
                }
            }
        },
        "HostBaichuanChat": {
            "display_name": "HostBaichuanChat",
            "description_url": "",
            "template": {
                "host_base_url": {
                    "display_name": "host_base_url"
                },
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "temperature"
                }
            }
        },
        "HostChatGLM": {
            "display_name": "HostChatGLM",
            "description_url": "",
            "template": {
                "host_base_url": {
                    "display_name": "host_base_url"
                },
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "temperature"
                }
            }
        },
        "HostLlama2Chat": {
            "display_name": "HostLlama2Chat",
            "description_url": "",
            "template": {
                "host_base_url": {
                    "display_name": "host_base_url"
                },
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "temperature"
                }
            }
        },
        "HostQwenChat": {
            "display_name": "HostQwenChat",
            "description_url": "",
            "template": {
                "host_base_url": {
                    "display_name": "host_base_url"
                },
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "temperature"
                }
            }
        },
        "HuggingFaceHub": {
            "display_name": "HuggingFaceHub",
            "description": "Wrapper around HuggingFaceHub  models.",
            "description_url": "",
            "template": {
                "huggingfacehub_api_token": {
                    "display_name": "HuggingFace Hub API Token"
                },
                "repo_id": {
                    "display_name": "repo_id"
                }
            }
        },
        "LlamaCpp": {
            "display_name": "LlamaCpp",
            "description": "llama.cpp model.",
            "description_url": "",
            "template": {
                "model_path": {
                    "display_name": "model_path"
                },
                "temperature": {
                    "display_name": "temperature"
                }
            }
        },
        "NPCsChatLLM": {
            "display_name": "NPCsChatLLM",
            "description": "`OpenAI` Chat large language models API.",
            "description_url": "",
            "template": {
                "api_base": {
                    "display_name": "api_base"
                },
                "api_key": {
                    "display_name": "api_key"
                },
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "temperature"
                }
            }
        },
        "OpenAI": {
            "display_name": "OpenAI",
            "description": "Wrapper around OpenAI large language models.",
            "description_url": "",
            "template": {
                "model_name": {
                    "display_name": "model_name",
                    "options": [
                        "text-davinci-003",
                        "text-davinci-002",
                        "text-curie-001",
                        "text-babbage-001",
                        "text-ada-001"
                    ]
                },
                "openai_api_base": {
                    "display_name": "OpenAI API Base"
                },
                "openai_api_key": {
                    "display_name": "OpenAI API Key"
                },
                "openai_proxy": {
                    "display_name": "OpenAI Proxy"
                },
                "temperature": {
                    "display_name": "temperature"
                }
            }
        },
        "ProxyChatLLM": {
            "display_name": "ProxyChatLLM",
            "description": "Wrapper around proxy Chat large language models.",
            "description_url": "",
            "template": {
                "elemai_api_key": {
                    "display_name": "elemai_api_key"
                },
                "elemai_base_url": {
                    "display_name": "elemai_base_url"
                },
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "temperature"
                }
            }
        },
        "VertexAI": {
            "display_name": "VertexAI",
            "description": "Wrapper around Google Vertex AI large language models.",
            "description_url": "",
            "template": {
                "credentials": {
                    "display_name": "credentials"
                },
                "location": {
                    "display_name": "location"
                },
                "max_retries": {
                    "display_name": "max_retries"
                },
                "metadata": {
                    "display_name": "metadata"
                },
                "model_name": {
                    "display_name": "model_name"
                },
                "project": {
                    "display_name": "project"
                },
                "request_parallelism": {
                    "display_name": "request_parallelism"
                },
                "temperature": {
                    "display_name": "temperature"
                }
            }
        }
    },
    "memories": {
        "ConversationBufferMemory": {
            "display_name": "ConversationBufferMemory",
            "description": "Buffer for storing conversation memory.",
            "description_url": "",
            "template": {
                "chat_memory": {
                    "display_name": "chat_memory"
                },
                "input_key": {
                    "display_name": "input_key"
                },
                "memory_key": {
                    "display_name": "memory_key"
                },
                "output_key": {
                    "display_name": "output_key"
                },
                "return_messages": {
                    "display_name": "return_messages"
                }
            },
            "output_types": [
                "ConversationBufferMemory"
            ]
        },
        "ConversationBufferWindowMemory": {
            "display_name": "ConversationBufferWindowMemory",
            "description": "Buffer for storing conversation memory.",
            "description_url": "",
            "template": {
                "chat_memory": {
                    "display_name": "chat_memory"
                },
                "input_key": {
                    "display_name": "input_key"
                },
                "k": {
                    "display_name": "Memory Size"
                },
                "memory_key": {
                    "display_name": "memory_key"
                },
                "output_key": {
                    "display_name": "output_key"
                },
                "return_messages": {
                    "display_name": "return_messages"
                }
            }
        },
        "ConversationEntityMemory": {
            "display_name": "ConversationEntityMemory",
            "description": "Entity extractor & summarizer memory.",
            "description_url": "",
            "template": {
                "chat_memory": {
                    "display_name": "chat_memory"
                },
                "llm": {
                    "display_name": "llm"
                },
                "chat_history_key": {
                    "display_name": "chat_history_key"
                },
                "input_key": {
                    "display_name": "input_key"
                },
                "k": {
                    "display_name": "Memory Size"
                },
                "output_key": {
                    "display_name": "output_key"
                },
                "return_messages": {
                    "display_name": "return_messages"
                }
            }
        },
        "ConversationKGMemory": {
            "display_name": "ConversationKGMemory",
            "description": "Knowledge graph memory for storing conversation memory.",
            "description_url": "",
            "template": {
                "chat_memory": {
                    "display_name": "chat_memory"
                },
                "llm": {
                    "display_name": "llm"
                },
                "input_key": {
                    "display_name": "input_key"
                },
                "k": {
                    "display_name": "Memory Size"
                },
                "memory_key": {
                    "display_name": "memory_key"
                },
                "output_key": {
                    "display_name": "output_key"
                },
                "return_messages": {
                    "display_name": "return_messages"
                }
            }
        },
        "ConversationSummaryMemory": {
            "display_name": "ConversationSummaryMemory",
            "description": "Conversation summarizer to memory.",
            "description_url": "",
            "template": {
                "chat_memory": {
                    "display_name": "chat_memory"
                },
                "llm": {
                    "display_name": "llm"
                },
                "input_key": {
                    "display_name": "input_key"
                },
                "memory_key": {
                    "display_name": "memory_key"
                },
                "output_key": {
                    "display_name": "output_key"
                },
                "return_messages": {
                    "display_name": "return_messages"
                }
            }
        },
        "MongoDBChatMessageHistory": {
            "display_name": "MongoDBChatMessageHistory",
            "description": "Memory store with MongoDB",
            "description_url": "",
            "template": {
                "collection_name": {
                    "display_name": "collection_name"
                },
                "connection_string": {
                    "display_name": "connection_string"
                },
                "database_name": {
                    "display_name": "database_name"
                },
                "session_id": {
                    "display_name": "session_id"
                }
            }
        },
        "PostgresChatMessageHistory": {
            "display_name": "PostgresChatMessageHistory",
            "description": "Memory store with Postgres",
            "description_url": "",
            "template": {
                "connection_string": {
                    "display_name": "connection_string"
                },
                "session_id": {
                    "display_name": "session_id"
                },
                "table_name": {
                    "display_name": "table_name"
                }
            }
        },
        "VectorStoreRetrieverMemory": {
            "display_name": "VectorStoreRetrieverMemory",
            "description": "Class for a VectorStore-backed memory object.",
            "description_url": "",
            "template": {
                "retriever": {
                    "display_name": "retriever"
                },
                "input_key": {
                    "display_name": "input_key"
                },
                "memory_key": {
                    "display_name": "memory_key"
                },
                "return_messages": {
                    "display_name": "return_messages"
                }
            }
        }
    },
    "output_parsers": {
        "ResponseSchema": {
            "display_name": "ResponseSchema",
            "description_url": "",
            "template": {
                "description": {
                    "display_name": "description"
                },
                "name": {
                    "display_name": "name"
                },
                "type": {
                    "display_name": "type"
                }
            }
        },
        "StructuredOutputParser": {
            "display_name": "StructuredOutputParser",
            "template": {
                "response_schemas": {
                    "display_name": "response_schemas"
                }
            }
        }
    },
    "prompts": {
        "ChatMessagePromptTemplate": {
            "display_name": "ChatMessagePromptTemplate",
            "description_url": "",
            "template": {
                "prompt": {
                    "display_name": "prompt"
                },
                "role": {
                    "display_name": "role"
                }
            }
        },
        "ChatPromptTemplate": {
            "display_name": "ChatPromptTemplate",
            "description_url": "",
            "template": {
                "messages": {
                    "display_name": "messages"
                },
                "output_parser": {
                    "display_name": "output_parser"
                }
            }
        },
        "HumanMessagePromptTemplate": {
            "display_name": "HumanMessagePromptTemplate",
            "description_url": "",
            "template": {
                "prompt": {
                    "display_name": "prompt"
                }
            }
        },
        "PromptTemplate": {
            "display_name": "PromptTemplate",
            "description": "Schema to represent a prompt for an LLM.",
            "description_url": "",
            "template": {
                "output_parser": {
                    "display_name": "output_parser"
                },
                "template": {
                    "display_name": "template"
                }
            }
        },
        "SystemMessagePromptTemplate": {
            "display_name": "SystemMessagePromptTemplate",
            "description_url": "",
            "template": {
                "prompt": {
                    "display_name": "prompt"
                }
            }
        }
    },
    "retrievers": {
        "MixEsVectorRetriever": {
            "display_name": "MixEsVectorRetriever",
            "description": "This class ensemble the results of es retriever and vector retriever.",
            "description_url": "",
            "template": {
                "keyword_retriever": {
                    "display_name": "keyword_retriever"
                },
                "vector_retriever": {
                    "display_name": "vector_retriever"
                },
                "combine_strategy": {
                    "display_name": "combine_strategy",
                    "options": [
                        "keyword_front",
                        "vector_front",
                        "mix"
                    ]
                }
            },
            "output_types": [
                "MixEsVectorRetriever"
            ]
        },
        "MultiQueryRetriever": {
            "display_name": "MultiQueryRetriever",
            "description": "Initialize from llm using default template.",
            "description_url": "",
            "template": {
                "llm": {
                    "display_name": "llm"
                },
                "prompt": {
                    "display_name": "prompt"
                },
                "retriever": {
                    "display_name": "retriever"
                }
            },
            "output_types": [
                "MultiQueryRetriever"
            ]
        }
    },
    "textsplitters": {
        "CharacterTextSplitter": {
            "display_name": "CharacterTextSplitter",
            "description": "Implementation of splitting text that looks at characters.",
            "description_url": "",
            "template": {
                "documents": {
                    "display_name": "documents"
                },
                "chunk_overlap": {
                    "display_name": "Chunk Overlap"
                },
                "chunk_size": {
                    "display_name": "Chunk Size"
                },
                "separator": {
                    "display_name": "separator"
                }
            },
            "output_types": [
                "Document"
            ]
        },
        "RecursiveCharacterTextSplitter": {
            "display_name": "RecursiveCharacterTextSplitter",
            "description": "Implementation of splitting text that looks at characters.",
            "description_url": "",
            "template": {
                "documents": {
                    "display_name": "documents"
                },
                "chunk_overlap": {
                    "display_name": "Chunk Overlap"
                },
                "chunk_size": {
                    "display_name": "Chunk Size"
                },
                "separator_type": {
                    "display_name": "Separator Type"
                },
                "separators": {
                    "display_name": "Separator"
                }
            },
            "output_types": [
                "Document"
            ]
        }
    },
    "toolkits": {
        "JsonToolkit": {
            "display_name": "JsonToolkit",
            "description": "Toolkit for interacting with a JSON spec.",
            "description_url": "",
            "template": {
                "spec": {
                    "display_name": "spec"
                }
            }
        },
        "OpenAPIToolkit": {
            "display_name": "OpenAPIToolkit",
            "description": "Toolkit for interacting with an OpenAPI API.",
            "description_url": "",
            "template": {
                "json_agent": {
                    "display_name": "json_agent"
                },
                "requests_wrapper": {
                    "display_name": "requests_wrapper"
                }
            }
        },
        "VectorStoreInfo": {
            "display_name": "VectorStoreInfo",
            "description": "Information about a vectorstore.",
            "description_url": "",
            "template": {
                "vectorstore": {
                    "display_name": "vectorstore"
                },
                "description": {
                    "display_name": "description"
                },
                "name": {
                    "display_name": "name"
                }
            }
        },
        "VectorStoreRouterToolkit": {
            "display_name": "VectorStoreRouterToolkit",
            "description": "Toolkit for routing between vector stores.",
            "description_url": "",
            "template": {
                "vectorstores": {
                    "display_name": "vectorstores"
                }
            }
        },
        "VectorStoreToolkit": {
            "display_name": "VectorStoreToolkit",
            "description": "Toolkit for interacting with a vector store.",
            "description_url": "",
            "template": {
                "vectorstore_info": {
                    "display_name": "vectorstore_info"
                }
            }
        }
    },
    "tools": {
        "BingSearchRun": {
            "display_name": "BingSearchRun",
            "description": "A wrapper around Bing Search. Useful for when you need to answer questions about current events. Input should be a search query.",
            "description_url": "",
            "template": {
                "api_wrapper": {
                    "display_name": "api_wrapper"
                },
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "Calculator": {
            "display_name": "Calculator",
            "description": "Useful for when you need to answer questions about math.",
            "description_url": "",
            "template": {
                "llm": {
                    "display_name": "llm"
                },
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "ComfyUIRun": {
            "display_name": "ComfyUIRun",
            "description_url": "",
            "template": {
                "api_wrapper": {
                    "display_name": "api_wrapper"
                },
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "GoogleSearchResults": {
            "display_name": "GoogleSearchResults",
            "description": "A wrapper around Google Search. Useful for when you need to answer questions about current events. Input should be a search query. Output is a JSON array of the query results",
            "description_url": "",
            "template": {
                "api_wrapper": {
                    "display_name": "api_wrapper"
                },
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "GoogleSearchRun": {
            "display_name": "GoogleSearchRun",
            "description": "A wrapper around Google Search. Useful for when you need to answer questions about current events. Input should be a search query.",
            "description_url": "",
            "template": {
                "api_wrapper": {
                    "display_name": "api_wrapper"
                },
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "GoogleSerperRun": {
            "display_name": "GoogleSerperRun",
            "description_url": "",
            "template": {
                "api_wrapper": {
                    "display_name": "api_wrapper"
                },
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "InfoSQLDatabaseTool": {
            "display_name": "InfoSQLDatabaseTool",
            "description": "\nInput to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables.\n\n    Example Input: 'table1, table2, table3'",
            "description_url": "",
            "template": {
                "db": {
                    "display_name": "db"
                },
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "JsonGetValueTool": {
            "display_name": "JsonGetValueTool",
            "description": "\n    Can be used to see value in string format at a given path.\n    Before calling this you should be SURE that the path to this exists.\n    The input is a text representation of the path to the dict in Python syntax (e.g. data[\"key1\"][0][\"key2\"]).\n    ",
            "description_url": "",
            "template": {
                "spec": {
                    "display_name": "spec"
                },
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "JsonListKeysTool": {
            "display_name": "JsonListKeysTool",
            "description": "\n    Can be used to see value in string format at a given path.\n    Before calling this you should be SURE that the path to this exists.\n    The input is a text representation of the path to the dict in Python syntax (e.g. data[\"key1\"][0][\"key2\"]).\n    ",
            "description_url": "",
            "template": {
                "spec": {
                    "display_name": "spec"
                },
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "JsonSpec": {
            "display_name": "JsonSpec",
            "description_url": "",
            "template": {
                "path": {
                    "display_name": "path"
                },
                "args_schema": {
                    "display_name": "args_schema"
                },
                "max_value_length": {
                    "display_name": "max_value_length"
                }
            }
        },
        "ListSQLDatabaseTool": {
            "display_name": "ListSQLDatabaseTool",
            "description": "Input is an empty string, output is a comma separated list of tables in the database.",
            "description_url": "",
            "template": {
                "db": {
                    "display_name": "db"
                },
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "News API": {
            "display_name": "News API",
            "description": "Use this when you want to get information about the top headlines of current news stories. The input should be a question in natural language that this API can answer.",
            "description_url": "",
            "template": {
                "llm": {
                    "display_name": "llm"
                },
                "args_schema": {
                    "display_name": "args_schema"
                },
                "news_api_key": {
                    "display_name": "news_api_key"
                }
            }
        },
        "PAL-MATH": {
            "display_name": "PAL-MATH",
            "description": "A language model that is really good at solving complex word math problems. Input should be a fully worded hard word math problem.",
            "description_url": "",
            "template": {
                "llm": {
                    "display_name": "llm"
                },
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "Podcast API": {
            "display_name": "Podcast API",
            "description": "Use the Listen Notes Podcast API to search all podcasts or episodes. The input should be a question in natural language that this API can answer.",
            "description_url": "",
            "template": {
                "llm": {
                    "display_name": "llm"
                },
                "args_schema": {
                    "display_name": "args_schema"
                },
                "listen_api_key": {
                    "display_name": "listen_api_key"
                }
            }
        },
        "PythonAstREPLTool": {
            "display_name": "PythonAstREPLTool",
            "description": "A Python shell. Use this to execute python commands. Input should be a valid python command. When using this tool, sometimes output is abbreviated - make sure it does not look abbreviated before using it in your answer.",
            "description_url": "",
            "template": {
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "PythonFunction": {
            "display_name": "PythonFunction",
            "description": "Python function to be executed.",
            "description_url": "",
            "template": {
                "code": {
                    "display_name": "code"
                }
            }
        },
        "PythonFunctionTool": {
            "display_name": "PythonAstREPLTool",
            "description": "Python function to be executed.",
            "description_url": "",
            "template": {
                "code": {
                    "display_name": "code"
                },
                "description": {
                    "display_name": "description"
                },
                "name": {
                    "display_name": "name"
                },
                "return_direct": {
                    "display_name": "return_direct"
                }
            }
        },
        "PythonREPLTool": {
            "display_name": "PythonREPLTool",
            "description": "A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.",
            "description_url": "",
            "template": {
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "QuerySQLDataBaseTool": {
            "display_name": "PythonREPLTool",
            "description": "\n    Input to this tool is a detailed and correct SQL query, output is a result from the database.\n    If the query is not correct, an error message will be returned.\n    If an error is returned, rewrite the query, check the query, and try again.\n    ",
            "description_url": "",
            "template": {
                "db": {
                    "display_name": "db"
                },
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "RequestsDeleteTool": {
            "display_name": "RequestsDeleteTool",
            "description": "A portal to the internet. Use this when you need to make a DELETE request to a URL. Input should be a specific url, and the output will be the text response of the DELETE request.",
            "description_url": "",
            "template": {
                "requests_wrapper": {
                    "display_name": "requests_wrapper"
                },
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "RequestsGetTool": {
            "display_name": "RequestsGetTool",
            "description": "A portal to the internet. Use this when you need to get specific content from a website. Input should be a  url (i.e. https://www.google.com). The output will be the text response of the GET request.",
            "description_url": "",
            "template": {
                "requests_wrapper": {
                    "display_name": "requests_wrapper"
                },
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "RequestsPatchTool": {
            "display_name": "RequestsPatchTool",
            "description": "Use this when you want to PATCH to a website.\n    Input should be a json string with two keys: \"url\" and \"data\".\n    The value of \"url\" should be a string, and the value of \"data\" should be a dictionary of \n    key-value pairs you want to PATCH to the url.\n    Be careful to always use double quotes for strings in the json string\n    The output will be the text response of the PATCH request.\n    ",
            "description_url": "",
            "template": {
                "requests_wrapper": {
                    "display_name": "requests_wrapper"
                },
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "RequestsPostTool": {
            "display_name": "RequestsPostTool",
            "description": "Use this when you want to POST to a website.\n    Input should be a json string with two keys: \"url\" and \"data\".\n    The value of \"url\" should be a string, and the value of \"data\" should be a dictionary of \n    key-value pairs you want to POST to the url.\n    Be careful to always use double quotes for strings in the json string\n    The output will be the text response of the POST request.\n    ",
            "description_url": "",
            "template": {
                "requests_wrapper": {
                    "display_name": "requests_wrapper"
                },
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "RequestsPutTool": {
            "display_name": "RequestsPutTool",
            "description": "Use this when you want to PUT to a website.\n    Input should be a json string with two keys: \"url\" and \"data\".\n    The value of \"url\" should be a string, and the value of \"data\" should be a dictionary of \n    key-value pairs you want to PUT to the url.\n    Be careful to always use double quotes for strings in the json string.\n    The output will be the text response of the PUT request.\n    ",
            "description_url": "",
            "template": {
                "requests_wrapper": {
                    "display_name": "requests_wrapper"
                },
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "Search": {
            "display_name": "Search",
            "description": "A search engine. Useful for when you need to answer questions about current events. Input should be a search query.",
            "description_url": "",
            "template": {
                "args_schema": {
                    "display_name": "args_schema"
                },
                "serpapi_api_key": {
                    "display_name": "serpapi_api_key"
                }
            }
        },
        "TMDB API": {
            "display_name": "TMDB API",
            "description": "Useful for when you want to get information from The Movie Database. The input should be a question in natural language that this API can answer.",
            "description_url": "",
            "template": {
                "llm": {
                    "display_name": "llm"
                },
                "args_schema": {
                    "display_name": "args_schema"
                },
                "tmdb_bearer_token": {
                    "display_name": "tmdb_bearer_token"
                }
            }
        },
        "Tool": {
            "display_name": "Tool",
            "description": "Converts a chain, agent or function into a tool.",
            "description_url": "",
            "template": {
                "func": {
                    "display_name": "func"
                },
                "args_schema": {
                    "display_name": "args_schema"
                },
                "description": {
                    "display_name": "description"
                },
                "name": {
                    "display_name": "name"
                },
                "return_direct": {
                    "display_name": "return_direct"
                }
            }
        },
        "WikipediaQueryRun": {
            "display_name": "WikipediaQueryRun",
            "description": "A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other subjects. Input should be a search query.",
            "description_url": "",
            "template": {
                "api_wrapper": {
                    "display_name": "api_wrapper"
                },
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        },
        "WolframAlphaQueryRun": {
            "display_name": "WolframAlphaQueryRun",
            "description": "A wrapper around Wolfram Alpha. Useful for when you need to answer questions about Math, Science, Technology, Culture, Society and Everyday Life. Input should be a search query.",
            "description_url": "",
            "template": {
                "api_wrapper": {
                    "display_name": "api_wrapper"
                },
                "args_schema": {
                    "display_name": "args_schema"
                }
            }
        }
    },
    "utilities": {
        "BingSearchAPIWrapper": {
            "display_name": "BingSearchAPIWrapper",
            "description": "Wrapper for Bing Search API.",
            "description_url": "",
            "template": {
                "bing_search_url": {
                    "display_name": "bing_search_url"
                },
                "bing_subscription_key": {
                    "display_name": "bing_subscription_key"
                }
            }
        },
        "ComfyUITxt2ImgAPIWrapper": {
            "display_name": "ComfyUITxt2ImgAPIWrapper",
            "description": "Wrapper for Comfy UI API.",
            "description_url": "",
            "template": {
                "comfy_ui_workflow": {
                    "display_name": "comfy_ui_workflow"
                },
                "comfy_ui_api_url": {
                    "display_name": "comfy_ui_api_url"
                },
                "comfy_ui_ws_url": {
                    "display_name": "comfy_ui_ws_url"
                }
            }
        },
        "GoogleSearchAPIWrapper": {
            "display_name": "GoogleSearchAPIWrapper",
            "description": "Wrapper for Google Search API.",
            "description_url": "",
            "template": {
                "google_api_key": {
                    "display_name": "google_api_key"
                }
            }
        },
        "GoogleSerperAPIWrapper": {
            "display_name": "GoogleSerperAPIWrapper",
            "description": "Wrapper around the Serper.dev Google Search API.",
            "description_url": "",
            "template": {
                "result_key_for_type": {
                    "display_name": "result_key_for_type"
                },
                "serper_api_key": {
                    "display_name": "serper_api_key"
                }
            }
        },
        "SearxSearchWrapper": {
            "display_name": "SearxSearchWrapper",
            "description": "Wrapper for Searx API.",
            "description_url": "",
            "template": {
                "headers": {
                    "display_name": "headers"
                }
            }
        },
        "SerpAPIWrapper": {
            "display_name": "SerpAPIWrapper",
            "description": "Wrapper around SerpAPI.",
            "description_url": "",
            "template": {
                "serpapi_api_key": {
                    "display_name": "serpapi_api_key"
                }
            }
        },
        "WikipediaAPIWrapper": {
            "display_name": "WikipediaAPIWrapper",
            "description": "Wrapper around WikipediaAPI.",
            "description_url": ""
        },
        "WolframAlphaAPIWrapper": {
            "display_name": "WolframAlphaAPIWrapper",
            "description": "Wrapper for Wolfram Alpha.",
            "description_url": ""
        }
    },
    "vectorstores": {
        "Chroma": {
            "display_name": "Chroma",
            "description": "Create a Chroma vectorstore from a raw documents.",
            "description_url": "",
            "template": {
                "documents": {
                    "display_name": "Documents"
                },
                "embedding": {
                    "display_name": "Embedding"
                },
                "collection_name": {
                    "display_name": "collection_name"
                },
                "persist": {
                    "display_name": "Persist"
                },
                "persist_directory": {
                    "display_name": "persist_directory"
                }
            }
        },
        "ElasticKeywordsSearch": {
            "display_name": "ElasticKeywordsSearch",
            "description": "Construct ElasticKeywordsSearch wrapper from raw documents.",
            "description_url": "",
            "template": {
                "documents": {
                    "display_name": "Documents"
                },
                "llm": {
                    "display_name": "LLM"
                },
                "prompt": {
                    "display_name": "prompt"
                },
                "elasticsearch_url": {
                    "display_name": "ES_connection_url"
                },
                "index_name": {
                    "display_name": "index_name"
                },
                "ssl_verify": {
                    "display_name": "ssl_verify"
                }
            }
        },
        "FAISS": {
            "display_name": "FAISS",
            "description": "Construct FAISS wrapper from raw documents.",
            "description_url": "",
            "template": {
                "documents": {
                    "display_name": "Documents"
                },
                "embedding": {
                    "display_name": "Embedding"
                },
                "folder_path": {
                    "display_name": "Local Path"
                },
                "index_name": {
                    "display_name": "Index Name"
                }
            }
        },
        "Milvus": {
            "display_name": "Milvus",
            "description": "Create a Milvus collection, indexes it with HNSW, and insert data.",
            "description_url": "",
            "template": {
                "documents": {
                    "display_name": "Documents"
                },
                "embedding": {
                    "display_name": "Embedding"
                },
                "collection_name": {
                    "display_name": "collection_name"
                },
                "connection_args": {
                    "display_name": "connection_args"
                }
            },
            "output_types": [
                "Milvus"
            ]
        },
        "MongoDBAtlasVectorSearch": {
            "display_name": "MongoDB Atlas",
            "description": "Create a Milvus collection, indexes it with HNSW, and insert data.",
            "description_url": "",
            "template": {
                "documents": {
                    "display_name": "Documents"
                },
                "embedding": {
                    "display_name": "Embedding"
                },
                "collection_name": {
                    "display_name": "Collection Name"
                },
                "db_name": {
                    "display_name": "Database Name"
                },
                "index_name": {
                    "display_name": "Index Name"
                },
                "mongodb_atlas_cluster_uri": {
                    "display_name": "MongoDB Atlas Cluster URI"
                }
            },
            "output_types": [
                "MongoDB Atlas"
            ]
        },
        "Pinecone": {
            "display_name": "Pinecone",
            "description": "Construct Pinecone wrapper from raw documents.",
            "description_url": "",
            "template": {
                "documents": {
                    "display_name": "Documents"
                },
                "embedding": {
                    "display_name": "Embedding"
                },
                "index_name": {
                    "display_name": "Index Name"
                },
                "namespace": {
                    "display_name": "namespace"
                }
            },
            "output_types": [
                "Pinecone"
            ]
        },
        "Qdrant": {
            "display_name": "Qdrant",
            "description": "Construct Qdrant wrapper from a list of texts.",
            "description_url": "",
            "template": {
                "documents": {
                    "display_name": "Documents"
                },
                "embedding": {
                    "display_name": "Embedding"
                },
                "api_key": {
                    "display_name": "API Key"
                },
                "collection_name": {
                    "display_name": "collection_name"
                },
                "location": {
                    "display_name": "location"
                }
            }
        },
        "SupabaseVectorStore": {
            "display_name": "Supabase",
            "description": "Return VectorStore initialized from texts and embeddings.",
            "description_url": "",
            "template": {
                "documents": {
                    "display_name": "Documents"
                },
                "embedding": {
                    "display_name": "Embedding"
                },
                "query_name": {
                    "display_name": "query_name"
                },
                "supabase_service_key": {
                    "display_name": "supabase_service_key"
                },
                "supabase_url": {
                    "display_name": "supabase_url"
                },
                "table_name": {
                    "display_name": "table_name"
                }
            }
        },
        "Weaviate": {
            "display_name": "Weaviate",
            "description": "Construct Weaviate wrapper from raw documents.",
            "description_url": "",
            "template": {
                "documents": {
                    "display_name": "Documents"
                },
                "embedding": {
                    "display_name": "Embedding"
                },
                "weaviate_url": {
                    "display_name": "weaviate_url"
                }
            }
        }
    },
    "wrappers": {
        "SQLDatabase": {
            "display_name": "SQLDatabase",
            "description": "Construct a SQLAlchemy engine from URI.",
            "description_url": "",
            "template": {
                "database_uri": {
                    "display_name": "database_uri"
                }
            }
        },
        "TextRequestsWrapper": {
            "display_name": "TextRequestsWrapper",
            "description": "Lightweight wrapper around requests library.",
            "description_url": "",
            "template": {
                "headers": {
                    "display_name": "headers"
                }
            }
        }
    },
    "port": "PORT"
}