exchanged relURL to absURL in metatags (#62)

* exchanged relURL to absURL in metatags * baseurl * fix * reverting netlify config * added social_preview_image variable * test immg * test other image * changed twitter summary_large_image to summary * more tests * src * added social preview images files for all articles * changed name to property on metatag * more changes, added an author to metatags * more meta for og:image
2026-09-26 14:38:30 +02:00 · 2022-08-23 16:55:11 +02:00
parent dd2fa1e8a9
commit 86868c6db1
19 changed files with 34 additions and 15 deletions
@@ -1,6 +1,6 @@
 [build]
 publish = "qdrant-landing/public"
-command = "cd qdrant-landing ; hugo --gc --minify -b '/'"
+command = "cd qdrant-landing ; hugo --gc --minify"

 [context.production.environment]
 HUGO_VERSION = "0.93.3"
@@ -8,7 +8,7 @@ HUGO_ENV = "production"
 HUGO_ENABLEGITINFO = "true"

 [context.deploy-preview]
-command = "cd qdrant-landing ; hugo --gc --minify --buildFuture -b '/'"
+command = "cd qdrant-landing ; hugo --gc --minify --buildFuture -b $DEPLOY_PRIME_URL"

 [context.deploy-preview.environment]
 HUGO_VERSION = "0.93.3"
@@ -1,4 +1,4 @@
-baseURL = "/"
+baseURL = "https://qdrant.tech"
 languageCode = "en-us"
 title = "Qdrant - Vector Search Engine"
 theme = "qdrant"
@@ -1,7 +1,8 @@
 ---
 title: Fine Tuning Similar Cars Search 
 short_description: "How to use similarity learning to search for similar cars"
-description: Learn how to train a similarity model that can retrieve similar car images in novel categories. 
+description: Learn how to train a similarity model that can retrieve similar car images in novel categories.
+social_preview_image: /articles_data/cars-recognition/social_preview.jpg
 preview_image: /articles_data/cars-recognition/preview.jpeg
 small_preview_image: /articles_data/cars-recognition/icon.svg
 weight: 10
@@ -1,7 +1,8 @@
 ---
 title: Metric Learning for Anomaly Detection
 short_description: "How to use metric learning to detect anomalies: quality assessment of coffee beans with just 200 labelled samples"
-description: Practical use of metric learning for anomaly detection. A way to match the results of a classification-based approach with only ~0.6% of the labeled data. 
+description: Practical use of metric learning for anomaly detection. A way to match the results of a classification-based approach with only ~0.6% of the labeled data.
+social_preview_image: /articles_data/detecting-coffee-anomalies/social_preview.jpg
 preview_image: /articles_data/detecting-coffee-anomalies/preview.png
 small_preview_image: /articles_data/detecting-coffee-anomalies/anomalies_icon.svg
 weight: 30
@@ -2,6 +2,7 @@
 title: Q&A with Similarity Learning
 short_description: A complete guide to building a Q&A system with similarity learning.
 description: A complete guide to building a Q&A system using Quaterion and SentenceTransformers.
+social_preview_image: /articles_data/faq-question-answering/social_preview.png
 preview_image: /articles_data/faq-question-answering/preview.png
 small_preview_image: /articles_data/faq-question-answering/icon.svg
 weight: 9
@@ -3,6 +3,7 @@ title: Filtrable HNSW
 short_description: How to make ANN search with custom filtering?
 description: How to make ANN search with custom filtering? Search in selected subsets without loosing the results.
 external_link: https://blog.vasnetsov.com/posts/categorical-hnsw/
+social_preview_image: /articles_data/filtrable-hnsw/social_preview.png
 preview_image: /articles_data/filtrable-hnsw/preview.png
 small_preview_image: /articles_data/filtrable-hnsw/global-network.svg
 weight: 60
@@ -3,6 +3,7 @@ title: Metric Learning Tips & Tricks
 short_description: How to train an object matching model and serve it in production.
 description: Practical recommendations on how to train a matching model and serve it in production. Even with no labeled data.
 external_link: https://vasnetsov93.medium.com/metric-learning-tips-n-tricks-2e4cfee6b75b
+social_preview_image: /articles_data/metric-learning-tips/social_preview.png
 preview_image: /articles_data/metric-learning-tips/preview.png
 small_preview_image: /articles_data/metric-learning-tips/scatter-graph.svg
 weight: 20
@@ -3,6 +3,7 @@ title: Neural Search Tutorial
 short_description: Step-by-step guide on how to build a neural search service.
 description: Our step-by-step guide on how to build a neural search service with BERT + Qdrant + FastAPI.
 external_link: https://blog.qdrant.tech/neural-search-tutorial-3f034ab13adc
+social_preview_image: /articles_data/neural-search-tutorial/social_preview.png
 preview_image: /articles_data/neural-search-tutorial/preview.png
 small_preview_image: /articles_data/neural-search-tutorial/tutorial.svg
 weight: 50
@@ -2,6 +2,7 @@
 title: Triplet Loss - Advanced Intro
 short_description: "What are the advantages of Triplet Loss and how to efficiently implement it?"
 description: "What are the advantages of Triplet Loss over Contrastive loss and how to efficiently implement it?"
+social_preview_image: /articles_data/triplet-loss/social_preview.png
 preview_image: /articles_data/triplet-loss/preview.png
 small_preview_image: /articles_data/triplet-loss/icon.svg
 weight: 30
@@ -81,9 +81,9 @@
 <meta property="og:title" content="{{ $title }}">

 <!-- Twitter Meta Tags -->
-<meta name="twitter:card" content="summary_large_image">
-<meta property="twitter:domain" content="{{ $domain }}">
-<meta property="twitter:url" content="{{ .Permalink }}">
+<meta name="twitter:card" content="summary">
+<meta name="twitter:domain" content="{{ $domain }}">
+<meta name="twitter:url" content="{{ .Permalink }}">
 <meta name="twitter:title" content="{{ $title }}">

 {{ if .Params.description }}
@@ -95,11 +95,23 @@
 {{ end }}

 <!-- Image -->
-{{ if .Params.preview_image }}
-<meta name="og:image" content="{{ .Params.preview_image | relURL }}">
-<meta name="twitter:image" content="{{ .Params.preview_image | relURL }}">
-<meta content="my-check!!">
+{{ if .Params.social_preview_image }}
+<meta name="image" property="og:image" content="{{ .Params.social_preview_image | absURL }}">
+<meta name="image" property="og:image:secure_url" content="{{ .Params.social_preview_image | absURL }}">
+<meta property="og:image:type" content="image/jpeg" />
+<meta property="og:image:width" content="1200" />
+<meta property="og:image:height" content="630" />
+<meta name="twitter:image:src" content="{{ .Params.social_preview_image | absURL }}">
 {{ else }}
-<meta name="og:image" content="{{ .Site.Params.preview_image | relURL }}">
-<meta name="twitter:image" content="{{ .Site.Params.preview_image | relURL }}">
+<meta name="image" property="og:image" content="{{ .Site.Params.preview_image | absURL }}">
+<meta name="image" property="og:image:secure_url" content="{{ .Site.Params.preview_image | absURL }}">
+<meta property="og:image:type" content="image/jpeg" />
+<meta property="og:image:width" content="1200" />
+<meta property="og:image:height" content="630" />
+<meta name="twitter:image:src" content="{{ .Site.Params.preview_image | absURL }}">
 {{ end }}
+
+<!-- Author -->
+{{ if .Params.author }}
+<meta name="author" content="{{ .Params.author }}">
+{{ end }}
@@ -2,7 +2,7 @@ name = "Qdrant"
 license = "MIT"
 licenselink = "https://github.com/yourname/yourtheme/blob/master/LICENSE"
 description = ""
-homepage = ""
+homepage = "https://qdrant.tech/"
 tags = []
 features = []
 min_version = "0.41.0"