Merge pull request #1746 from qdrant/static-embeddings

move static embeddings

qdrant-landing/content/documentation/database-tutorials/_index.md

@@ -18,4 +18,5 @@ partition: qdrant
 | [Backup and Restore Qdrant Collections Using Snapshots](/documentation/database-tutorials/create-snapshot/) |
 | [Load and Search Hugging Face Datasets with Qdrant](/documentation/database-tutorials/huggingface-datasets/)                       | 
 | [Using Qdrant’s Async API for Efficient Python Applications](/documentation/database-tutorials/async-api/)            | 
-| [Qdrant Migration Guide](/documentation/database-tutorials/migration/)            | 
+| [Qdrant Migration Guide](/documentation/database-tutorials/migration/)            | 
+| [Static Embeddings. Should you pay attention?](/documentation/database-tutorials/static-embeddings/)            | 

qdrant-landing/content/documentation/database-tutorials/static-embeddings.md

@@ -1,15 +1,10 @@
 ---
-draft: false
-title: "Static Embeddings: should you pay attention?"
-slug: static-embeddings
-short_description: "Static embeddings are a thing back! Is the encoding speedup worth a try? We checked that!"
-description: "Static embeddings are a thing back! Is the encoding speedup worth a try? We checked that!"
-preview_image: /blog/static-embeddings/preview.png
-date: 2025-01-17T09:13:00.000Z
-author: Kacper Łukawski
-featured: false
+title: Static Embeddings. Should you pay attention?
+weight: 181
+aliases:
+  - /blog/static-embeddings/
 ---
-
+# Static Embeddings: should you pay attention?
 In the world of resource-constrained computing, a quiet revolution is taking place. While transformers dominate 
 leaderboards with their impressive capabilities, static embeddings are making an unexpected comeback, offering 
 remarkable speed improvements with surprisingly small quality trade-offs. **We evaluated how Qdrant users can benefit
@@ -24,7 +19,7 @@ quality of the older methods, such as word2vec or GloVe, which could only create
 word. As a result, the word "bank" would have identical representation in the context of "river bank" and "financial 
 institution".
 
-![Static embeddings](/blog/static-embeddings/financial-river-bank.png)
+![Static embeddings](/articles_data/static-embeddings/financial-river-bank.png)
 
 Transformer-based models would represent the word "bank" differently in each of the contexts. However, transformers come 
 with a cost. They are computationally expensive and usually require a lot of memory, although the embeddings models