From 5140a4d769fb54ede2561e99bae1e70d9ced1ca4 Mon Sep 17 00:00:00 2001 From: Dhravya Date: Sat, 22 Jun 2024 18:13:35 -0500 Subject: [PATCH] deleted chatpage --- apps/web/app/(dash)/chat/page.tsx | 120 ------------------------------ 1 file changed, 120 deletions(-) delete mode 100644 apps/web/app/(dash)/chat/page.tsx diff --git a/apps/web/app/(dash)/chat/page.tsx b/apps/web/app/(dash)/chat/page.tsx deleted file mode 100644 index 1ce59ec5..00000000 --- a/apps/web/app/(dash)/chat/page.tsx +++ /dev/null @@ -1,120 +0,0 @@ -import ChatWindow from "./chatWindow"; -import { chatSearchParamsCache } from "@/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"); - -function Page({ - searchParams, -}: { - searchParams: Record; -}) { - const { firstTime, q, spaces } = chatSearchParamsCache.parse(searchParams); - - console.log(spaces); - - return ; - return ( -
- {/* */} - -
- {/* single q&A */} - {Array.from({ length: 1 }).map((_, i) => ( -
- {/* header */} -
- {/* query */} -

- Why is Retrieval-Augmented Generation important? -

-
- - {/* response */} -
- {/* related memories */} -
- {/* section header */} -
-

Related memories

- -
- - {/* section content */} - {/* collection of memories */} -
- {/* related memory */} - {Array.from({ length: 3 }).map((_, i) => ( -
-

Webpage

-

- What is RAG? - Retrieval-Augmented Generation Explained - - AWS -

-
- ))} -
-
- - {/* summary */} -
- {/* section header */} -
-

Summary

- -
- - {/* section content */} -
-

- 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 */} -
- {/* speak response */} - - {/* copy response */} - -
-
-
-
-
- ))} -
- -
- -
-
- ); -} - -export default Page;