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This ready-to-use component provides an out of the box solution for adding RAG-Powered chat interfaces to ","maintainers":[{"email":"mehmet@upstash.com","name":"mdogan"},{"email":"bill@upstash.com","name":"buggyhunter"},{"email":"cahidardaooz@gmail.com","name":"cahidarda"},{"email":"fahreddin@upstash.com","name":"fahreddin.ozcan"},{"email":"enes@upstash.com","name":"upstashnpm"},{"email":"hello@joshtriedcoding.com","name":"joshtc"},{"email":"burak@upstash.com","name":"burak-ups"},{"email":"shannon@upstash.com","name":"shannonr"}],"readme":"# RAG Chat Widget\n\nA customizable Reach chat widget that combines Upstash Vector for similarity search, Together AI for LLM, and Vercel AI SDK for streaming responses. This ready-to-use component provides an out of the box solution for adding RAG-Powered chat interfaces to your Next.js application.\n\n<table>\n  <tr>\n    <td align=\"center\">\n      <img src=\"./public/images/widget-closed.png\" alt=\"RAG Chat Widget - Closed State\" width=\"300\"/><br/>\n      <em>Closed State</em>\n    </td>\n    <td align=\"center\">\n      <img src=\"./public/images/widget-open.png\" alt=\"RAG Chat Widget - Open State\" width=\"300\"/><br/>\n      <em>Open State</em>\n    </td>\n  </tr>\n</table>\n\n## Features\n\n⚡ Streaming responses support\n\n💻 Server actions\n\n📱 Responsive design\n\n🔍 Real-time context retrieval\n\n💾 Persistent chat history\n\n🎨 Fully customizable UI components\n\n🎨 Dark/light mode support\n\n## Installation\n\n```bash\n# Using npm\nnpm install @upstash/rag-chat-widget\n\n# Using pnpm\npnpm add @upstash/rag-chat-widget\n\n# Using yarn\nyarn add @upstash/rag-chat-widget\n```\n\n## Quick Start\n\n### 1. Environment Variables\n\nCreate an Upstash Vector database and set up the environment variables as below. If you don't have an account, you can start by going to [Upstash Console](https://console.upstash.com).\n\nChoose an embedding model when creating an index in Upstash Vector.\n\n```\nUPSTASH_VECTOR_REST_URL=\nUPSTASH_VECTOR_REST_TOKEN=\n\nOPENAI_API_KEY=\n\nTOGETHER_API_KEY=\n\n# Optional\nTOGETHER_MODEL=\n```\n\n### 2. Configure Styles\n\nIn your `tailwind.config.ts` file, add the configuration below:\n\n```ts\nimport type { Config } from \"tailwindcss\";\n\nexport default {\n  content: [\"./node_modules/@upstash/rag-chat-widget/**/*.{js,mjs}\"],\n} satisfies Config;\n```\n\n### 3. Implementation\n\nThe RAG Chat Widget can be integrated into your application using two straightforward approaches. Choose the method that best fits your project structure:\n\n#### 1. Using a Dedicated Component File (Recommended)\n\nCreate a seperate component file with the `use client` directive, then import and use it anywhere in your application.\n\n```jsx\n// components/widget.tsx\n\"use client\";\n\nimport { ChatWidget } from \"@upstash/rag-chat-widget\";\n\nexport const Widget = () => {\n  return <ChatWidget />;\n};\n```\n\n```jsx\n// page.tsx\nimport { Widget } from \"./components/widget\";\n\nexport default function Home() {\n  return (\n    <>\n      <Widget />\n      <p>Home</p>\n    </>\n  );\n}\n```\n\n#### 2. Direct Integration in Client Components\n\nAlternatively, import and use the **ChatWidget** directly in your client-side pages.\n\n```jsx\n// page.tsx\n\"use client\";\nimport { ChatWidget } from \"@upstash/rag-chat-widget\";\n\nexport default function Home() {\n  return (\n    <>\n      <ChatWidget />\n      <p>Home</p>\n    </>\n  );\n}\n```\n\n### 4. Choosing Chat Model\n\nIt's possible to choose one of the [together.ai](https://www.together.ai/) models for the chat.\nDefault model is `meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo`. You can configure it in the environment variables.\n\n```\nTOGETHER_MODEL=\"deepseek-ai/DeepSeek-V3\"\n```\n\n## Adding Content\n\nYou can add content to your RAG Chat widget in several ways:\n\n<details>\n<summary>1. Using RAG Chat SDK</summary>\n\nThe SDK provides methods to add various types of content programmatically:\n\n```ts\nimport { RAGChat, openai } from \"@upstash/rag-chat\";\n\nexport const ragChat = new RAGChat({\n  model: openai(\"gpt-4-turbo\"),\n});\n// Add text content\nawait ragChat.context.add(\"Your text content here\");\n\n// Add PDF documents\nawait ragChat.context.add({\n  type: \"pdf\",\n  fileSource: \"./path/to/document.pdf\",\n});\n\n// Add web content\nawait ragChat.context.add({\n  type: \"html\",\n  source: \"https://your-website.com\",\n});\n```\n\nFor more detailed examples and options, check out the [RAG Chat documentation](https://upstash.com/docs/vector/sdks/rag-chat/gettingstarted).\n\n</details>\n\n<details>\n<summary>2. Using Upstash Vector UI</summary>\n\nYou can also manage your content directly through the Upstash Vector Console:\n\n1. Navigate to [Upstash Console](http://console.upstash.com/vector).\n2. Go to details page of the Vector database.\n3. Navigate to **Databrowser Tab**.\n4. Here, you can either upload a PDF, or use on of our sample datasets.\n\n<img src=\"./public/images/vector-databrowser.png\" alt=\"Vector Databrowser\"/><br/>\n\n</details>\n\n## Contributing\n\nWe welcome contributions! Please see our contributing guidelines for more details.\n\n## License\n\nMIT License - see the LICENSE file for details.\n","readmeFilename":"README.md"}