chore: open graph

denser-org · May 28, 2024 · 46e80ce · 46e80ce
1 parent f036ce3
commit 46e80ce
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Showing 10 changed files with 19 additions and 20 deletions.
diff --git a/experiments/config_local.yaml b/experiments/config_local.yaml
@@ -34,4 +34,4 @@ rerank:
 output_prefix: ./denser_output_retriever/
 
 max_doc_size: 0
-max_query_size: 10000
+max_query_size: 10000
diff --git a/tests/test_data/cpws_2021_10_top10.csv b/tests/test_data/cpws_2021_10_top10.csv
diff --git a/www/app/layout.tsx b/www/app/layout.tsx
@@ -4,7 +4,7 @@ import { GeistMono } from "geist/font/mono"
 import { GeistSans } from "geist/font/sans"
 import { Londrina_Solid } from "next/font/google"
 
-import Script from "next/script"
+import { GoogleAnalytics } from "@next/third-parties/google"
 import { Footer } from "../components/footer"
 import "./global.css"
 
@@ -23,7 +23,7 @@ export const metadata = createMetadata({
     template: "DenserRetriever • %s",
     default: "DenserRetriever • Docs",
   },
-  description: "Bookmarks, Read-it-later, and RSS-Feeds",
+  description: "Cutting-edge AI Retriever for RAG",
   metadataBase: baseUrl,
 })
 
@@ -45,15 +45,13 @@ export default function Layout({ children }: { children: ReactNode }) {
         <link rel="icon" href="/favicon.ico" sizes="any" />
         <link rel="apple-touch-icon" href="/apple-touch-icon.png" />
       </head>
-      {process.env.NODE_ENV === "production" && (
-        <Script data-api="/a/e" data-domain="docs.denser.ai" src="/p.js" />
-      )}
       <body className="overflow-x-hidden">
         <RootProvider>
           {children}
           <Footer />
         </RootProvider>
       </body>
+      <GoogleAnalytics gaId="G-LBFKFW4G31" />
     </html>
   )
 }
diff --git a/www/app/opengraph-image.alt.txt b/www/app/opengraph-image.alt.txt
@@ -0,0 +1 @@
+Denser Retriever
diff --git a/www/app/opengraph-image.png b/www/app/opengraph-image.png
diff --git a/www/app/twitter-image.alt.txt b/www/app/twitter-image.alt.txt
@@ -0,0 +1 @@
+Denser Retriever
diff --git a/www/app/twitter-image.png b/www/app/twitter-image.png
diff --git a/www/components/home/join-cta.tsx b/www/components/home/join-cta.tsx
@@ -12,9 +12,9 @@ export function JoinCTA() {
             </div>
           </div>
           <div className="flex flex-col items-stretch justify-center w-full col-span-2 gap-4">
-            <h4 className="mt-8 text-4xl font-semibold leading-none text-left lg:text-5xl dark:text-white text-neutral-600 font-display">
+            <h5 className="mt-8 text-4xl font-semibold leading-none text-left lg:text-5xl dark:text-white text-neutral-600 font-display">
               DenserRetriever v1 Beta coming!
-            </h4>
+            </h5>
             <p className="mt-3 text-base leading-relaxed text-left text-gray-500 dark:text-neutral-100">
               You can try out DenserRetriever in your self-hosted machine,
               with an extremely simple docker setup.

diff --git a/www/content/docs/experiments/training.mdx b/www/content/docs/experiments/training.mdx
@@ -148,5 +148,3 @@ On the contrary, the xgboost models can effectively estimate the feature importa
 Xgboost model has a nice feature to estimate the feature importance. We plot the feature importance in the following picture. It shows that the normalized vector search score (VS Norm2) is the most important feature to predict if a passage is relevant or not. The normalized reranker feature (RR Norm1) is the second most important feature.
 
 ![Feature importance](./feature_importance.png)
-
-
diff --git a/www/package.json b/www/package.json
@@ -11,6 +11,7 @@
     "generate:openapi": "node generate-openapi.mjs"
   },
   "dependencies": {
+    "@next/third-parties": "^14.2.3",
     "@phosphor-icons/react": "^2.1.4",
     "fumadocs-core": "10.1.2",
     "fumadocs-mdx": "8.2.7",
Original file line number	Diff line number	Diff line change
Expand Up		@@ -148,5 +148,3 @@ On the contrary, the xgboost models can effectively estimate the feature importa
		Xgboost model has a nice feature to estimate the feature importance. We plot the feature importance in the following picture. It shows that the normalized vector search score (VS Norm2) is the most important feature to predict if a passage is relevant or not. The normalized reranker feature (RR Norm1) is the second most important feature.

		![Feature importance](./feature_importance.png)