diff --git a/apps/web/app/(dash)/chat/page.tsx b/apps/web/app/(dash)/chat/page.tsx index f98c761f..12b1bd2a 100644 --- a/apps/web/app/(dash)/chat/page.tsx +++ b/apps/web/app/(dash)/chat/page.tsx @@ -1,6 +1,9 @@ import ChatWindow from "./chatWindow"; import { chatSearchParamsCache } from "../../helpers/lib/searchParams"; import { ChevronDownIcon, ClipboardIcon, SpeakerWaveIcon } from '@heroicons/react/24/outline' +import Image from "next/image"; +import { ArrowRightIcon } from "@repo/ui/icons"; +import QueryInput from "@repo/ui/components/QueryInput"; // @ts-expect-error await import("katex/dist/katex.min.css"); @@ -14,80 +17,88 @@ function Page({ console.log(spaces); return ( -
What is RAG? - Retrieval-Augmented Generation Explained - AWS
-- Retrieval-Augmented Generation is crucial because it combines the strengths of retrieval-based methods, ensuring relevance and accuracy, with generation-based models, enabling creativity and flexibility. By integrating retrieval mechanisms, it addresses data sparsity issues, improves content relevance, offers fine-tuned control over output, handles ambiguity, and allows for continual learning, making it highly adaptable and effective across various natural language processing tasks and domains. -
+ {/* related memories */} +What is RAG? - Retrieval-Augmented Generation Explained - AWS
++ Retrieval-Augmented Generation is crucial because it combines the strengths of retrieval-based methods, ensuring relevance and accuracy, with generation-based models, enabling creativity and flexibility. By integrating retrieval mechanisms, it addresses data sparsity issues, improves content relevance, offers fine-tuned control over output, handles ambiguity, and allows for continual learning, making it highly adaptable and effective across various natural language processing tasks and domains. +
+ + {/* response actions */} ++ no results found. +
+ } + /> */} + {/*