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data"},"license":"AGPL-3.0","keywords":["nextjs","next","typescript","antd","chatbot"],"description":"SmartData Embedded Agent","maintainers":[{"name":"thelias","email":"akandetoluwalase@gmail.com"}],"readme":"![npm](https://img.shields.io/npm/v/@smartdatahq/embedded-agent)\r\n![license](https://img.shields.io/badge/License-AGPL%20v3-blue.svg)\r\n\r\n# Installation Guide: Embedded Agent\r\n\r\nThe `@smartdatahq/embedded-agent` package is a powerful tool for integrating AI-driven conversational agents into your web applications. This guide will walk you through the installation and setup process.\r\n\r\n## Prerequisites\r\n\r\nBefore using the `@smartdatahq/embedded-agent` package, ensure the following:\r\n\r\n1. You have Node.js (version 14 or higher) and npm installed on your system.\r\n2. You have a valid **identifier** to connect to the `@smartdatahq/embedded-agent` service.\r\n   Make sure you contact us to get your identifier.\r\n\r\n---\r\n\r\n## Installation\r\n\r\nTo install the package, use npm or yarn:\r\n\r\n### Using npm\r\n\r\n```bash\r\nnpm install @smartdatahq/embedded-agent\r\n```\r\n\r\n### Using yarn\r\n\r\n```bash\r\nyarn add @smartdatahq/embedded-agent\r\n```\r\n\r\n## Basic Usage\r\n\r\nHere is an example of how to integrate the embedded agent into your React application:\r\n\r\n##### Import the Component\r\n\r\n```javascript\r\nimport { EmbeddedAgent } from \"@smartdatahq/embedded-agent\";\r\nimport \"@smartdatahq/embedded-agent/dist/index.css\";\r\n```\r\n\r\nIf your application is mostly SSR, you might need to dynamically import the package since it needs to run on the browser.\r\nFor example, in next.j, you would want to import it like this\r\n\r\n```javascript\r\nconst EmbeddedAgent = dynamic(\r\n  () => import(\"@smartdatahq/embedded-agent\").then((mod) => mod.EmbeddedAgent),\r\n  {\r\n    ssr: false,\r\n  },\r\n);\r\n```\r\n\r\n##### Configure the embedded agent\r\n\r\nUse the following example to set up the agent in your application:\r\n\r\n```typescript\r\nconst App = () => {\r\n  const [minimized, setMinimized] = useState(true)\r\n  const handleSearchResults = (result: any) => {\r\n    // Search Results\r\n    console.log('Search Results:', result);\r\n  };\r\n\r\n  const handleContextUpdate = (schema: any) => {\r\n    // Updated Context Schema to keep track of the filters applied/current state\r\n    console.log('Updated Context Schema:', schema);\r\n  };\r\n\r\n  return (\r\n    <div style={{ height: minimized ? 120 : 400 }}>\r\n      <h1>My Embedded Agent Application</h1>\r\n      <EmbeddedAgent\r\n        identifier=\"YOUR_IDENTIFIER\" // Replace with your unique identifier\r\n        onConversationFeedback={handleSearchResults}\r\n        onContextJsonUpdate={handleContextUpdate}\r\n        minimized={minimized}\r\n        onMinimizedChange={() => setMinimized(!minimized)}\r\n        contextSchema={{\r\n            \"$schema\": \"http://json-schema.org/draft-07/schema#\",\r\n            \"type\": \"object\",\r\n            \"properties\": {\r\n                 \"maximum_price\": {\r\n                  \"type\": [\"integer\", \"null\"],\r\n                  \"minimum\": 0,\r\n                  \"description\": \"Maximum Price for the article. Optional.\"\r\n                },\r\n                \"manufacturer\": {\r\n                    \"type\": \"array\",\r\n                    \"description\": \"Manufacturer name (e.g., Samsung, Apple).\",\r\n                    \"items\": {\r\n                        \"type\": \"string\",\r\n                         \"enum\": [\"Samsung\", \"Apple\", \"Nokia\", \"Philips\"],\r\n                    },\r\n\r\n                },\r\n                \"screen_type\": {\r\n                    \"type\": \"string\",\r\n                    \"enum\": [\"OLED\", \"LCD\", \"AMOLED\"],\r\n                    \"description\": \"Type of screen (e.g., OLED, LCD, AMOLED).\"\r\n                },\r\n                \"color\": {\r\n                    \"type\": \"string\",\r\n                    \"description\": \"Color of the product (e.g., black, white).\"\r\n                },\r\n                \"size\": {\r\n                    \"type\": \"string\",\r\n                    \"description\": \"Size of the product (e.g., 13-inch, 15-inch).\"\r\n                },\r\n                \"manufacturing_date\": {\r\n                  \"type\": \"string\",\r\n                  \"format\": \"date\",\r\n                \"description\": \"Manufacturing date of this product\"\r\n                },\r\n            },\r\n            \"required\": [\"price\", \"manufacturer\", \"manufacturing_date\", \"screen_type\", \"color\", \"size\"],\r\n        }}\r\n      />\r\n    </div>\r\n  );\r\n};\r\n```\r\n\r\n### API\r\n\r\n##### identifier (Required)\r\n\r\n    type: string\r\n    Description: Unique identifier for connecting to the embedded agent server.\r\n\r\n##### contextSchema\r\n\r\n    type: Record<string, any>\r\n    Description: Defines the schema for extracting parameters from the conversation.\r\n\r\nNotes:\r\nThe contextSchema is defined in compliance with JSON Schema draft-07.\r\nThis ensures the agent uses a well-defined schema for extracting and validating context data.\r\nLearn more here: https://tour.json-schema.org/\r\n\r\n##### onContextJsonUpdate\r\n\r\n    Type: (schema: any) => void\r\n    Description: Callback function triggered when the context schema updates.\r\n\r\n##### onConversationFeedback\r\n\r\n    Type: (result: any) => void\r\n    Description: Callback function triggered with a result when the agent resolves the user’s request.\r\n\r\n### Additionnal API\r\n\r\nYou can also customize the embedded agent with additional props, including:\\\r\n **className**: Custom class for styling the embedded agent container.\\\r\n **minimized**: Default minimized state of the embedded agent. If being used, you need to update it to match the value you get from the onMinimizedChange function.\\\r\n **onMinimizedChange**: Callback for changes in the minimized state.\\\r\n **conversationId**: Unique conversation ID for the session. This is generated internally if not provided. It should preferably be a UUID string.\\\r\n **existingFilters**: Existing filters to be applied to the conversation.\\\r\n **isRendered**: boolean. You can set this to false if you don't want the agent to show on render. Instead, we will display a message that can be clicked to load the agent\\\r\n **onChatbotRender**: This function is called when the agent is rendered. It only runs if you set render to false and manually click the message that displays in place of the agent.\\\r\n **onError**: Callback for handling errors.\r\n","readmeFilename":"README.md"}