Add Latest/Featured Articles on main page

2026-10-02 17:38:31 +02:00 · 2026-06-23 09:59:42 -07:00
parent d4a001c903
commit 16efcd9124
62 changed files with 103 additions and 56 deletions
@@ -8,6 +8,7 @@ author: Thierry Damiba
 draft: false
 date: 2025-10-26T00:00:00.000Z
 category: rag-and-agents
+weight: 10
 ---
 ## Overview

@@ -4,7 +4,7 @@ short_description: "Will agentic RAG replace linear RAG? Learn how to build agen
 description: "Agents are a new paradigm in AI, and they are changing how we build RAG systems. Learn how to build agents with Qdrant and which framework to choose."
 preview_dir: /articles_data/agentic-rag/preview
 social_preview_image: /articles_data/agentic-rag/preview/social_preview.jpg
-weight: -150
+weight: 20
 author: Kacper Łukawski
 author_link: https://www.kacperlukawski.com
 date: 2024-11-22T00:00:00.000Z
@@ -14,6 +14,7 @@ tags:
  - Machine Learning
  - Information Retrieval
 category: mastering-search
+weight: 100
 ---

 # How to Optimize Vector Search Using Batch Search in Qdrant 0.10.0
@@ -19,7 +19,7 @@ tags:
  - OpenAI
  - binary quantization
  - embeddings
-weight: -130
+weight: 40

 aliases: [ /blog/binary-quantization-openai/ ]
 category: search-quality
@@ -5,7 +5,7 @@ description: "Binary Quantization is a newly introduced mechanism of reducing th
 social_preview_image: /articles_data/binary-quantization/social_preview.png
 small_preview_image: /articles_data/binary-quantization/binary-quantization-icon.svg
 preview_dir: /articles_data/binary-quantization/preview
-weight: -40
+weight: 70
 author: Nirant Kasliwal
 author_link: https://nirantk.com/about/ 
 date: 2023-09-18T13:00:00+03:00
@@ -4,7 +4,7 @@ short_description: "Introducing next evolutionary step in lexical search."
 description: "Introducing BM42 - a new sparse embedding approach, which combines the benefits of exact keyword search with the intelligence of transformers."
 social_preview_image: /articles_data/bm42/social-preview.jpg
 preview_dir: /articles_data/bm42/preview
-weight: -140
+weight: 40
 author: Andrey Vasnetsov
 date: 2024-07-01T12:00:00+03:00
 draft: false
@@ -5,7 +5,7 @@ description: Learn how to train a similarity model that can retrieve similar car
 social_preview_image: /articles_data/cars-recognition/preview/social_preview.jpg
 small_preview_image: /articles_data/cars-recognition/icon.svg
 preview_dir: /articles_data/cars-recognition/preview
-weight: 10
+weight: 60
 author: Yusuf Sarıgöz
 author_link: https://medium.com/@yusufsarigoz
 date: 2022-06-28T13:00:00+03:00
@@ -4,7 +4,7 @@ short_description: "Secure Your Data with Qdrant: Implementing RBAC"
 description: Discover how Qdrant's Role-Based Access Control (RBAC) ensures data privacy and compliance for your AI applications. Build secure and scalable systems with ease. Read more now!
 social_preview_image: /articles_data/data-privacy/preview/social_preview.jpg # This image will be used in social media previews, should be 1200x630px. Required.
 preview_dir: /articles_data/data-privacy/preview # This directory contains images that will be used in the article preview. They can be generated from one image. Read more below. Required.
-weight: -110 # This is the order of the article in the list of articles at the footer. The lower the number, the higher the article will be in the list.
+weight: 50
 author: Qdrant Team # Author of the article. Required.
 author_link: https://qdrant.tech/ # Link to the author's page. Required.
 date: 2024-06-18T08:00:00-03:00 # Date of the article. Required.
@@ -5,7 +5,7 @@ description: Improving quality of text-and-images datasets on the online furnitu
 preview_dir: /articles_data/dataset-quality/preview
 social_preview_image: /articles_data/dataset-quality/preview/social_preview.jpg
 small_preview_image: /articles_data/dataset-quality/icon.svg
-weight: 8
+weight: 50
 author: George Panchuk
 author_link: https://medium.com/@george.panchuk
 date: 2022-07-18T10:18:00.000Z
@@ -5,7 +5,7 @@ description: "Why vector search requires a dedicated service."
 social_preview_image: /articles_data/dedicated-service/social-preview.png
 small_preview_image: /articles_data/dedicated-service/preview/icon.svg
 preview_dir: /articles_data/dedicated-service/preview
-weight: -70
+weight: 90
 author: Andrey Vasnetsov
 author_link: https://vasnetsov.com/
 date: 2023-11-30T10:00:00+03:00
@@ -4,7 +4,7 @@ short_description: "Why add-on vector search looks good — until you actually u
 description: "Why add-on vector search looks good — until you actually use it."
 social_preview_image: /articles_data/dedicated-vector-search/preview/social_preview.jpg
 preview_dir: /articles_data/dedicated-vector-search/preview
-weight: -170
+weight: 20
 author: Evgeniya Sukhodolskaya & Andrey Vasnetsov
 date: 2025-02-17T10:00:00+03:00
 draft: false
@@ -5,7 +5,7 @@ description: Practical use of metric learning for anomaly detection. A way to ma
 social_preview_image: /articles_data/detecting-coffee-anomalies/preview/social_preview.jpg
 preview_dir: /articles_data/detecting-coffee-anomalies/preview
 small_preview_image: /articles_data/detecting-coffee-anomalies/anomalies_icon.svg
-weight: 30
+weight: 70
 author: Yusuf Sarıgöz
 author_link: https://medium.com/@yusufsarigoz
 date: 2022-05-04T13:00:00+03:00
@@ -5,7 +5,7 @@ description: Discovery Search, an innovative way to constrain the vector space i
 social_preview_image: /articles_data/discovery-search/social_preview.jpg 
 small_preview_image: /articles_data/discovery-search/icon.svg 
 preview_dir: /articles_data/discovery-search/preview 
-weight: -110 
+weight: 20
 author: Luis Cossío 
 author_link: https://coszio.github.io 
 date: 2024-01-31T08:00:00-03:00 
@@ -4,7 +4,7 @@ short_description: "Efficient visualization and clusterization of high-dimension
 description: "Explore your data under a new angle with Qdrant's tools for dimensionality reduction, clusterization, and visualization."
 social_preview_image: /articles_data/distance-based-exploration/social-preview.jpg
 preview_dir: /articles_data/distance-based-exploration/preview
-weight: -250
+weight: 10
 author: Andrey Vasnetsov
 date: 2025-03-11T12:00:00+03:00
 draft: false
@@ -5,7 +5,7 @@ description: Learn when and how to use layer recycling to achieve different perf
 preview_dir: /articles_data/embedding-recycling/preview
 small_preview_image: /articles_data/embedding-recycling/icon.svg
 social_preview_image: /articles_data/embedding-recycling/preview/social_preview.jpg
-weight: 10
+weight: 50
 author: Yusuf Sarıgöz
 author_link: https://medium.com/@yusufsarigoz
 date: 2022-08-23T13:00:00+03:00
@@ -5,7 +5,7 @@ description: A complete guide to building a Q&A system using Quaterion and Sente
 social_preview_image: /articles_data/faq-question-answering/preview/social_preview.jpg
 preview_dir: /articles_data/faq-question-answering/preview
 small_preview_image: /articles_data/faq-question-answering/icon.svg
-weight: 9
+weight: 60
 author: George Panchuk
 author_link: https://medium.com/@george.panchuk
 date: 2022-06-28T08:57:07.604Z
@@ -5,7 +5,7 @@ description: "Learn how to accurately and efficiently create text embeddings wit
 social_preview_image: /articles_data/fastembed/preview/social_preview.jpg
 small_preview_image: /articles_data/fastembed/preview/lightning.svg
 preview_dir: /articles_data/fastembed/preview
-weight: -60
+weight: 10
 author: Nirant Kasliwal
 author_link: https://nirantk.com/about/ 
 date: 2023-10-18T10:00:00+03:00
@@ -6,7 +6,7 @@ description: How to make ANN search with custom filtering? Search in selected su
 social_preview_image: /articles_data/filterable-hnsw/social_preview.jpg
 preview_dir: /articles_data/filterable-hnsw/preview
 small_preview_image: /articles_data/filterable-hnsw/global-network.svg
-weight: 60
+weight: 40
 date: 2019-11-24T22:44:08+03:00
 author: Andrei Vasnetsov
 author_link: https://blog.vasnetsov.com/
@@ -5,7 +5,7 @@ description: Feeling hungry? Find the perfect meal with Qdrant's multimodal sema
 preview_dir: /articles_data/food-discovery-demo/preview
 social_preview_image: /articles_data/food-discovery-demo/preview/social_preview.jpg
 small_preview_image: /articles_data/food-discovery-demo/icon.svg
-weight: -30
+weight: 30
 author: Kacper Łukawski
 author_link: https://medium.com/@lukawskikacper
 date: 2023-09-05T11:32:00.000Z
@@ -4,7 +4,7 @@ short_description: "Why and how we built our own key-value store."
 description: "Why and how we built our own key-value store. A short technical report on our procedure and results."
 preview_dir: /articles_data/gridstore-key-value-storage/preview
 social_preview_image: /articles_data/gridstore-key-value-storage/social_preview.png
-weight: -150
+weight: 10
 author: Luis Cossio, Arnaud Gourlay & David Myriel
 date: 2025-02-05T00:00:00.000Z
 category: qdrant-internals
@@ -9,6 +9,7 @@ author_link: https://www.kacperlukawski.com
 date: 2025-07-15T00:00:00.000Z
 category: core-concepts
 draft: false
+weight: 10
 ---

 No matter if you are just beginning your journey in the world of vector search, or you are a seasoned practitioner, you 
@@ -4,7 +4,7 @@ short_description: "Merging different search methods to improve the search quali
 description: "Our new Query API allows you to build a hybrid search system that uses different search methods to improve search quality & experience. Learn more here."
 preview_dir: /articles_data/hybrid-search/preview
 social_preview_image: /articles_data/hybrid-search/social-preview.png
-weight: -150
+weight: 80
 author: Kacper Łukawski
 author_link: https://kacperlukawski.com
 date: 2024-07-25T00:00:00.000Z
@@ -4,7 +4,7 @@ short_description: "Learn how immutable data structures improve vector search pe
 description: "Learn how immutable data structures improve vector search performance in Qdrant."
 social_preview_image: /articles_data/immutable-data-structures/social_preview.png
 preview_dir: /articles_data/immutable-data-structures/preview
-weight: -200
+weight: 20
 author: Andrey Vasnetsov
 date: 2024-08-20T10:45:00+02:00
 draft: false
@@ -4,7 +4,7 @@ short_description: "Best practices to optimize memory usage during high-volume v
 description: "Efficient memory management is key when handling large-scale vector data. Learn how to optimize memory consumption during bulk uploads in Qdrant and keep your deployments performant under heavy load."  
 preview_dir: /articles_data/indexing-optimization/preview
 social_preview_image: /articles_data/indexing-optimization/preview/social_preview.jpg  
-weight: -155
+weight: 30
 author: Sabrina Aquino  
 date: 2025-02-13T00:00:00.000Z  
 category: production-ops
@@ -5,7 +5,7 @@ description: "Slow disk decelerating your Qdrant deployment? Get on top of IO ov
 social_preview_image: /articles_data/io_uring/social_preview.png
 small_preview_image: /articles_data/io_uring/io_uring-icon.svg
 preview_dir: /articles_data/io_uring/preview
-weight: 3
+weight: 30
 author: Andre Bogus
 author_link: https://llogiq.github.io
 date: 2023-06-21T09:45:00+02:00
@@ -5,7 +5,7 @@ description: "We combined LangChain, a pre-trained LLM from OpenAI, SentenceTran
 social_preview_image: /articles_data/langchain-integration/social_preview.png
 small_preview_image: /articles_data/langchain-integration/chain.svg
 preview_dir: /articles_data/langchain-integration/preview
-weight: 6
+weight: 40
 author: Kacper Łukawski
 author_link: https://medium.com/@lukawskikacper
 date: 2023-01-31T10:53:20+01:00
@@ -4,7 +4,7 @@ short_description: "Standard dense embedding models perform surprisingly well in
 description: "We recently discovered that embedding models can become late interaction models & can perform surprisingly well in some scenarios. See what we learned here."
 preview_dir: /articles_data/late-interaction-models/preview
 social_preview_image: /articles_data/late-interaction-models/social-preview.png
-weight: -160
+weight: 30
 author: Kacper Łukawski
 author_link: https://kacperlukawski.com
 date: 2024-08-14T00:00:00.000Z
@@ -5,7 +5,7 @@ description: How to properly measure RAM usage and optimize Qdrant for memory co
 social_preview_image: /articles_data/memory-consumption/preview/social_preview.jpg
 preview_dir: /articles_data/memory-consumption/preview
 small_preview_image: /articles_data/memory-consumption/icon.svg
-weight: 7
+weight: 100
 author: Andrei Vasnetsov
 author_link: https://blog.vasnetsov.com/
 date: 2022-12-07T10:18:00.000Z
@@ -6,7 +6,7 @@ description: Practical recommendations on how to train a matching model and serv
 social_preview_image: /articles_data/metric-learning-tips/preview/social_preview.jpg
 preview_dir: /articles_data/metric-learning-tips/preview
 small_preview_image: /articles_data/metric-learning-tips/scatter-graph.svg
-weight: 20
+weight: 90
 author: Andrei Vasnetsov
 author_link: https://blog.vasnetsov.com/
 date: 2021-05-15T10:18:00.000Z
@@ -4,7 +4,7 @@ short_description: "Our attempt to learn from drawbacks of modern sparse neural
 description: "Introducing miniCOIL, a lightweight sparse neural retriever capable of generalization."
 social_preview_image: /articles_data/minicoil/preview/social_preview.jpg
 preview_dir: /articles_data/minicoil/preview
-weight: -190
+weight: 10
 author: Evgeniya Sukhodolskaya
 date: 2025-05-13T00:00:00+03:00
 draft: false
@@ -4,7 +4,7 @@ short_description: ""
 description: "A comprehensive guide to modern sparse neural retrievers: COIL, TILDEv2, SPLADE, and more. Find out how they work and learn how to use them effectively." 
 preview_dir: /articles_data/modern-sparse-neural-retrieval/preview
 social_preview_image: /articles_data/modern-sparse-neural-retrieval/social-preview.png
-weight: -213 
+weight: 20
 author: Evgeniya Sukhodolskaya
 date: 2024-10-23T00:00:00.000Z
 tags: 
@@ -5,7 +5,7 @@ description: "Discover how multitenancy and custom sharding in Qdrant can stream
 social_preview_image: /articles_data/multitenancy/social_preview.png
 preview_dir: /articles_data/multitenancy/preview
 small_preview_image: /articles_data/multitenancy/icon.svg
-weight: -120
+weight: 60
 author: David Myriel
 date: 2024-02-06T13:21:00.000Z
 draft: false
@@ -8,6 +8,7 @@ author: Kacper Łukawski
 author_link: https://kacperlukawski.com
 date: 2025-09-05T00:00:00.000Z
 category: mastering-search
+weight: 60
 ---

 ## What are MUVERA Embeddings?
@@ -6,7 +6,7 @@ description: Discover the power of neural search. Learn what neural search is an
 social_preview_image: /articles_data/neural-search-tutorial/social_preview.jpg
 preview_dir: /articles_data/neural-search-tutorial/preview
 small_preview_image: /articles_data/neural-search-tutorial/tutorial.svg
-weight: 50
+weight: 70
 author: Andrey Vasnetsov
 author_link: https://blog.vasnetsov.com/
 date: 2021-06-10T10:18:00.000Z
@@ -5,7 +5,7 @@ description: "Discover product quantization in vector search technology. Learn h
 social_preview_image: /articles_data/product-quantization/social_preview.png
 small_preview_image: /articles_data/product-quantization/product-quantization-icon.svg
 preview_dir: /articles_data/product-quantization/preview
-weight: 4
+weight: 80
 author: Kacper Łukawski
 author_link: https://medium.com/@lukawskikacper
 date: 2023-05-30T09:45:00+02:00
@@ -5,7 +5,7 @@ description: "End-to-end Question Answering system for the biomedical data with
 social_preview_image: /articles_data/qa-with-cohere-and-qdrant/social_preview.png
 small_preview_image: /articles_data/qa-with-cohere-and-qdrant/q-and-a-article-icon.svg
 preview_dir: /articles_data/qa-with-cohere-and-qdrant/preview
-weight: 7
+weight: 50
 author: Kacper Łukawski
 author_link: https://medium.com/@lukawskikacper
 date: 2022-11-29T15:45:00+01:00
@@ -5,7 +5,7 @@ description: Uncover the necessity of vector databases for RAG and learn how Qdr
 social_preview_image: /articles_data/rag-is-dead/preview/social_preview.jpg 
 small_preview_image: /articles_data/rag-is-dead/icon.svg 
 preview_dir: /articles_data/rag-is-dead/preview 
-weight: -131
+weight: 60
 author: David Myriel 
 author_link: https://github.com/davidmyriel
 date: 2024-02-27T00:00:00.000Z
@@ -5,7 +5,7 @@ description: Learn how Qdrant-powered RAG applications can be tested and iterati
 social_preview_image: /articles_data/rapid-rag-optimization-with-qdrant-and-quotient/preview/social_preview.jpg
 small_preview_image: /articles_data/rapid-rag-optimization-with-qdrant-and-quotient/icon.svg
 preview_dir: /articles_data/rapid-rag-optimization-with-qdrant-and-quotient/preview
-weight: -131
+weight: 30
 author: Atita Arora
 author_link: https://github.com/atarora
 date: 2024-06-12T00:00:00.000Z
@@ -4,7 +4,7 @@ short_description: "The story behind the vector search-native relevance feedback
 description: "The story behind the vector search-native relevance feedback feature, available since 1.17.0, which increases the relevance of search results universally, cheaply, and at scale."
 social_preview_image: /articles_data/relevance-feedback/preview/social_preview.jpg
 preview_dir: /articles_data/relevance-feedback/preview
-weight: -170
+weight: 10
 author: Evgeniya Sukhodolskaya
 date: 2026-02-20T00:00:00+03:00
 draft: false
@@ -5,7 +5,7 @@ description: "Discover the efficiency of scalar quantization for optimized data
 social_preview_image: /articles_data/scalar-quantization/social_preview.png
 small_preview_image: /articles_data/scalar-quantization/scalar-quantization-icon.svg
 preview_dir: /articles_data/scalar-quantization/preview
-weight: 5
+weight: 90
 author: Kacper Łukawski
 author_link: https://medium.com/@lukawskikacper
 date: 2023-03-27T10:45:00+01:00
@@ -5,7 +5,7 @@ description: To show off Qdrant's performance, we show how to do a quick search-
 social_preview_image: /articles_data/search-as-you-type/preview/social_preview.jpg
 small_preview_image: /articles_data/search-as-you-type/icon.svg
 preview_dir: /articles_data/search-as-you-type/preview
-weight: -2
+weight: 20
 author: Andre Bogus
 author_link: https://llogiq.github.io
 date: 2023-08-14T00:00:00+01:00
@@ -4,7 +4,7 @@ short_description: "Incorporating relevance feedback into discovery search, an o
 description: "Relerance feedback: from ancient history to LLMs. Why relevance feedback techniques are good on paper but not popular in neural search, and what we can do about it."
 social_preview_image: /articles_data/search-feedback-loop/preview/social_preview.jpg
 preview_dir: /articles_data/search-feedback-loop/preview
-weight: -170
+weight: 20
 author: Evgeniya Sukhodolskaya
 date: 2025-03-27T00:00:00+03:00
 draft: false
@@ -19,6 +19,7 @@ tags:
  - data retrieval
  - efficient data storage
 category: rag-and-agents
+weight: 30
 ---

 ## What is Semantic Cache?
@@ -5,7 +5,7 @@ description: "Create a serverless semantic search engine using nothing but Qdran
 social_preview_image: /articles_data/serverless/social_preview.png
 small_preview_image: /articles_data/serverless/icon.svg
 preview_dir: /articles_data/serverless/preview
-weight: 1
+weight: 30
 author: Andre Bogus
 author_link: https://llogiq.github.io
 date: 2023-07-12T10:00:00+01:00
@@ -4,7 +4,7 @@ short_description: "Dense embeddings blur exact matches. Sparse embeddings keep
 description: "Part 1 of a 5-part series on fine-tuning SPLADE sparse embeddings for e-commerce search. Learn how sparse embeddings outperform BM25 and dense models for product search, how SPLADE works, and why Qdrant's native sparse vector support matters."
 preview_dir: /articles_data/sparse-embeddings-ecommerce-part-1/preview
 social_preview_image: /articles_data/sparse-embeddings-ecommerce-part-1/preview/social_preview.jpg
-weight: -200
+weight: 10
 author: Thierry Damiba
 author_link: https://github.com/thierrydamiba
 date: 2026-03-09T00:00:00.000Z
@@ -4,7 +4,7 @@ short_description: "Train a SPLADE model on Amazon's ESCI dataset using Modal's
 description: "Part 2 of a 5-part series on fine-tuning SPLADE sparse embeddings for e-commerce search. Build a training pipeline on Modal with persistent checkpoints, SpladeLoss, and hyperparameter sweeps."
 preview_dir: /articles_data/sparse-embeddings-ecommerce-part-2/preview
 social_preview_image: /articles_data/sparse-embeddings-ecommerce-part-2/preview/social_preview.jpg
-weight: -199
+weight: 20
 author: Thierry Damiba
 author_link: https://github.com/thierrydamiba
 date: 2026-03-09T00:00:00.000Z
@@ -4,7 +4,7 @@ short_description: "Evaluate fine-tuned SPLADE with Qdrant and boost results wit
 description: "Part 3 of a 5-part series on fine-tuning SPLADE sparse embeddings for e-commerce search. Index products in Qdrant, run retrieval benchmarks, and implement ANCE-inspired hard negative mining for a 28% improvement over BM25."
 preview_dir: /articles_data/sparse-embeddings-ecommerce-part-3/preview
 social_preview_image: /articles_data/sparse-embeddings-ecommerce-part-3/preview/social_preview.jpg
-weight: -198
+weight: 30
 author: Thierry Damiba
 author_link: https://github.com/thierrydamiba
 date: 2026-03-09T00:00:00.000Z
@@ -4,7 +4,7 @@ short_description: "When to fine-tune sparse embeddings and how far to specializ
 description: "Part 4 of a 5-part series on fine-tuning SPLADE sparse embeddings for e-commerce search. Test cross-domain generalization, train a multi-domain model, and decide when to specialize vs generalize."
 preview_dir: /articles_data/sparse-embeddings-ecommerce-part-4/preview
 social_preview_image: /articles_data/sparse-embeddings-ecommerce-part-4/preview/social_preview.jpg
-weight: -197
+weight: 40
 author: Thierry Damiba
 author_link: https://github.com/thierrydamiba
 date: 2026-03-09T00:00:00.000Z
@@ -4,7 +4,7 @@ short_description: "One command to fine-tune SPLADE for your catalog. No ML pipe
 description: "Part 5 of the sparse embeddings series. We packaged the entire training pipeline from Parts 1-4 into an open-source CLI and web dashboard that fine-tunes SPLADE models for any product catalog in minutes."
 preview_dir: /articles_data/sparse-embeddings-ecommerce-part-5/preview
 social_preview_image: /articles_data/sparse-embeddings-ecommerce-part-5/preview/social_preview.jpg
-weight: -196
+weight: 50
 author: Thierry Damiba
 author_link: https://github.com/thierrydamiba
 date: 2026-03-09T00:00:00.000Z
@@ -5,7 +5,7 @@ description: "Learn what sparse vectors are, how they work, and their importance
 social_preview_image: /articles_data/sparse-vectors/social_preview.png
 small_preview_image: /articles_data/sparse-vectors/sparse-vectors-icon.svg
 preview_dir: /articles_data/sparse-vectors/preview
-weight: -100
+weight: 80
 author: Nirant Kasliwal
 author_link: https://nirantk.com/about 
 date: 2023-12-09T13:00:00+03:00
@@ -15,6 +15,7 @@ tags:
  - Search
  - Similarity Search
 category: mastering-search
+weight: 90
 ---

 # How to Optimize Vector Storage by Storing Multiple Vectors Per Object
@@ -5,7 +5,7 @@ description: "What are the advantages of Triplet Loss over Contrastive loss and
 social_preview_image: /articles_data/triplet-loss/social_preview.jpg
 preview_dir: /articles_data/triplet-loss/preview
 small_preview_image: /articles_data/triplet-loss/icon.svg
-weight: 30
+weight: 80
 author: Yusuf Sarıgöz
 author_link: https://medium.com/@yusufsarigoz
 date: 2022-03-24T15:12:00+03:00
@@ -15,7 +15,7 @@ keywords:
  - performance
  - benchmarks
 category: production-ops
-weight: -200
+weight: 10
 ---

 If you run production vector workloads, you already know the compression ladder in Qdrant: float32 is the baseline, **Scalar Quantization (SQ)** compresses vectors by 4x with almost no recall hit, and **Binary Quantization (BQ)** packs vectors at 16x or 32x.
@@ -4,7 +4,7 @@ short_description: "Merging different search methods to improve the search quali
 description: "Learn everything about filtering in Qdrant. Discover key tricks and best practices to boost semantic search performance and reduce Qdrant's resource usage."
 preview_dir: /articles_data/vector-search-filtering/preview
 social_preview_image: /articles_data/vector-search-filtering/social-preview.png
-weight: -200
+weight: 70
 author: Sabrina Aquino, David Myriel
 author_link: 
 date: 2024-09-10T00:00:00.000Z
@@ -8,6 +8,7 @@ author: David Myriel
 author_link: 
 date: 2025-04-30T00:00:00.000Z
 category: production-ops
+weight: 20
 ---

 ## What Does it Take to Run Search in Production?
@@ -4,7 +4,7 @@ short_description: "Combine optimization methods to improve resource usage."
 description: "Learn how to get the most from Qdrant's optimization features. Discover key tricks and best practices to boost vector search performance and reduce Qdrant's resource usage."
 preview_dir: /articles_data/vector-search-resource-optimization/preview
 social_preview_image: /articles_data/vector-search-resource-optimization/preview/social_preview.jpg
-weight: -200
+weight: 40
 author: David Myriel
 category: production-ops
 date: 2025-02-09T00:00:00.000Z
@@ -5,7 +5,7 @@ description: Discover how vector similarity expands data exploration beyond full
 preview_dir: /articles_data/vector-similarity-beyond-search/preview
 small_preview_image: /articles_data/vector-similarity-beyond-search/icon.svg
 social_preview_image: /articles_data/vector-similarity-beyond-search/preview/social_preview.jpg
-weight: -1
+weight: 40
 author: Luis Cossío
 author_link: https://coszio.github.io/
 date: 2023-08-08T08:00:00+03:00
@@ -5,7 +5,7 @@ slug: what-are-embeddings?
 short_description: Explore the power of vector embeddings. Learn to use numerical machine learning representations to build a personalized Neural Search Service with Fastembed.
 description: Discover the power of vector embeddings. Learn how to harness the potential of numerical machine learning representations to create a personalized Neural Search Service with FastEmbed.
 preview_dir: /articles_data/what-are-embeddings/preview
-weight: -102
+weight: 70
 social_preview_image: /articles_data/what-are-embeddings/preview/social-preview.jpg
 small_preview_image: /articles_data/what-are-embeddings/icon.svg
 date: 2024-02-06T15:29:33-03:00
@@ -4,7 +4,7 @@ draft: false
 short_description:  What is a Vector Database? Use Cases & Examples | Qdrant
 description: Discover what a vector database is, its core functionalities, and real-world applications.
 preview_dir: /articles_data/what-is-a-vector-database/preview
-weight: -211
+weight: 30
 social_preview_image: /articles_data/what-is-a-vector-database/preview/social_preview.png
 small_preview_image: /articles_data/what-is-a-vector-database/icon.svg
 date: 2024-10-09T09:29:33-03:00
@@ -5,7 +5,7 @@ slug: what-is-vector-quantization
 short_description:  What is Vector Quantization? Methods & Examples 
 description: In this article, we'll teach you about compression methods like Scalar, Product, and Binary Quantization. Learn how to choose the best method for your specific application.
 preview_dir: /articles_data/what-is-vector-quantization/preview
-weight: -210
+weight: 40
 social_preview_image: /articles_data/what-is-vector-quantization/preview/social-preview.jpg
 date: 2024-09-25T09:29:33-03:00
 author: Sabrina Aquino 
@@ -5,7 +5,7 @@ slug: what-is-rag-in-ai?
 short_description:  What is RAG? 
 description: Explore how RAG enables LLMs to retrieve and utilize relevant external data when generating responses, rather than being limited to their original training data alone.
 preview_dir: /articles_data/what-is-rag-in-ai/preview
-weight: -150
+weight: 50
 social_preview_image: /articles_data/what-is-rag-in-ai/preview/social_preview.jpg
 small_preview_image: /articles_data/what-is-rag-in-ai/icon.svg
 date: 2024-03-19T9:29:33-03:00