initial category reorganization

2026-10-08 20:38:31 +02:00 · 2026-06-22 22:08:34 -07:00
parent ffedde0770
commit 37de08db16
82 changed files with 186 additions and 132 deletions
@@ -7,7 +7,7 @@ social_preview_image: /articles_data/agentic-builders-guide/preview/social_previ
 author: Thierry Damiba
 draft: false
 date: 2025-10-26T00:00:00.000Z
-category: rag-and-genai
+category: rag-and-agents
 ---
 ## Overview
@@ -8,7 +8,7 @@ weight: -150
 author: Kacper Łukawski
 author_link: https://www.kacperlukawski.com
 date: 2024-11-22T00:00:00.000Z
-category: rag-and-genai
+category: rag-and-agents
 ---
 Standard [Retrieval Augmented Generation](/articles/what-is-rag-in-ai/) follows a predictable, linear path: receive 
@@ -13,7 +13,7 @@ tags:
  - Vector Database
  - Machine Learning
  - Information Retrieval
-category: vector-search-manuals
+category: mastering-search
 ---
 # How to Optimize Vector Search Using Batch Search in Qdrant 0.10.0
@@ -22,7 +22,7 @@ tags:
 weight: -130
 aliases: [ /blog/binary-quantization-openai/ ]
-category: practicle-examples
+category: search-quality
 ---
 OpenAI Ada-003 embeddings are a powerful tool for natural language processing (NLP). However, the size of the embeddings are a challenge, especially with real-time search and retrieval. In this article, we explore how you can use Qdrant's Binary Quantization to enhance the performance and efficiency of OpenAI embeddings.
@@ -14,7 +14,7 @@ keywords:
  - vector search
  - binary quantization
  - memory optimization
-category: qdrant-internals
+category: production-ops
 ---
 # Optimizing High-Dimensional Vectors with Binary Quantization
@@ -12,7 +12,7 @@ keywords:
  - hybrid search
  - sparse embeddings
  - bm25
-category: machine-learning
+category: embedding-research
 ---
 <aside role="status">
@@ -11,7 +11,8 @@ author_link: https://medium.com/@yusufsarigoz
 date: 2022-06-28T13:00:00+03:00
 draft: false
 # aliases: [ /articles/cars-recognition/ ]
-category: machine-learning
+category: embedding-research
 hideFromList: true
 ---
 Supervised classification is one of the most widely used training objectives in machine learning,
@@ -0,0 +1,9 @@
 ---
 title: Core Concepts
 short_description: "Foundational concepts for vector search: embeddings, vector databases, quantization, sparse vectors, and RAG."
 description: Start here to understand the building blocks of vector search. Learn what vector databases, embeddings, quantization, and retrieval-augmented generation are and how they fit together.
 category: core-concepts
 url: /articles/core-concepts/
 isCategoryPage: true
 weight: 10
 ---
@@ -15,7 +15,7 @@ keywords:
  - reranking
  - fastembed
  - qsoc'24
-category: machine-learning
+hideFromList: true
 ---
 ## Introduction
@@ -5,5 +5,5 @@ description: Learn how you can leverage vector similarity beyond just search. Re
 category: data-exploration
 url: /articles/data-exploration/
 isCategoryPage: true
-weight: 30
+weight: 80
 ---
@@ -15,7 +15,7 @@ keywords: # Keywords for SEO
  - Secure AI Data Management
  - Qdrant Data Security
  - Enterprise Data Compliance
-category: vector-search-manuals
+category: production-ops
 ---
 Data stored in vector databases is often proprietary to the enterprise and may include sensitive information like customer records, legal contracts, electronic health records (EHR), financial data, and intellectual property. Moreover, strong security measures become critical to safeguarding this data. If the data stored in a vector database is not secured, it may open a vulnerability known as "[embedding inversion attack](https://arxiv.org/abs/2004.00053)," where malicious actors could potentially [reconstruct the original data from the embeddings](https://arxiv.org/pdf/2305.03010) themselves. 
@@ -15,7 +15,7 @@ keywords:
    - vector search
    - best practices
    - anti-patterns
-category: qdrant-internals
+category: core-concepts
 ---
@@ -12,7 +12,7 @@ keywords:
    - system architecture
    - vector search
    - vector database 
-category: qdrant-internals
+category: core-concepts
 ---
 Any problem with even a bit of complexity requires a specialized solution. You can use a Swiss Army knife to open a bottle or poke a hole in a cardboard box, but you will need an axe to chop wood — the same goes for software.
@@ -0,0 +1,9 @@
 ---
 title: Demos & Tutorials
 short_description: "Step-by-step tutorials and demos with Qdrant: neural search, serverless deployments, and framework integrations."
 description: Learn by building. Follow hands-on tutorials and demos covering neural search, serverless deployments, search-as-you-type, and integrations with popular frameworks.
 category: demos-and-tutorials
 url: /articles/demos-and-tutorials/
 isCategoryPage: true
 weight: 90
 ---
@@ -10,8 +10,9 @@ author: Yusuf Sarıgöz
 author_link: https://medium.com/@yusufsarigoz
 date: 2022-05-04T13:00:00+03:00
 draft: false
-category: machine-learning
+category: embedding-research
 # aliases: [ /articles/detecting-coffee-anomalies/ ]
 hideFromList: true
 ---
 Anomaly detection is a thirsting yet challenging task that has numerous use cases across various industries.
@@ -17,7 +17,7 @@ keywords:
    - vector similarity
    - tsne
    - qdrant data visualization
-category: ecosystem
+hideFromList: true
 ---
@@ -1,9 +0,0 @@
 ---
 title: Ecosystem
 short_description: "Articles covering Qdrant's ecosystem: integrations, embedding providers, frameworks, and tools that pair with vector search."
 description: Tools, libraries and integrations around Qdrant vector search engine.
 category: ecosystem
 url: /articles/ecosystem/
 isCategoryPage: true
 weight: 
 ---
@@ -11,7 +11,8 @@ author_link: https://medium.com/@yusufsarigoz
 date: 2022-08-23T13:00:00+03:00
 draft: false
 aliases: [ /articles/embedding-recycler/ ]
-category: machine-learning
+category: embedding-research
 hideFromList: true
 ---
 A recent [paper](https://arxiv.org/abs/2207.04993)
@@ -0,0 +1,9 @@
 ---
 title: Embedding Research
 short_description: "Research on embeddings and neural retrieval: sparse models, late interaction, metric learning, and new baselines."
 description: Explore the research behind modern embeddings and neural retrieval. Dive into sparse neural models, late interaction, metric learning, and new baselines for hybrid search.
 category: embedding-research
 url: /articles/embedding-research/
 isCategoryPage: true
 weight: 60
 ---
@@ -9,8 +9,9 @@ weight: 9
 author: George Panchuk
 author_link: https://medium.com/@george.panchuk
 date: 2022-06-28T08:57:07.604Z
-category: practicle-examples
+category: demos-and-tutorials
 # aliases: [ /articles/faq-question-answering/ ]
 hideFromList: true
 ---
 # Question-answering system with Similarity Learning and Quaterion
@@ -19,7 +19,7 @@ keywords:
    - embeddings
    - ONNX Runtime
    - quantized embedding model
-category: ecosystem
+category: demos-and-tutorials
 ---
 Data Science and Machine Learning practitioners often find themselves navigating through a labyrinth of models, libraries, and frameworks. Which model to choose, what embedding size, and how to approach tokenizing, are just some questions you are faced with when starting your work. We understood how many data scientists wanted an easier and more intuitive means to do their embedding work. This is why we built FastEmbed, a Python library engineered for speed, efficiency, and usability. We have created easy to use default workflows, handling the 80% use cases in NLP embedding.
@@ -9,7 +9,7 @@ weight: -30
 author: Kacper Łukawski
 author_link: https://medium.com/@lukawskikacper
 date: 2023-09-05T11:32:00.000Z
-category: practicle-examples
+category: data-exploration
 ---
 Not every search journey begins with a specific destination in mind. Sometimes, you just want to explore and see what’s out there and what you might like.
@@ -15,7 +15,7 @@ keywords:
    - geo polygon
    - search condition
    - gsoc'23
-category: qdrant-internals
+hideFromList: true
 ---
@@ -7,7 +7,7 @@ social_preview_image: /articles_data/how-to-choose-an-embedding-model/preview/so
 author: Kacper Łukawski
 author_link: https://www.kacperlukawski.com
 date: 2025-07-15T00:00:00.000Z
-category: vector-search-manuals
+category: core-concepts
 draft: false
 ---
@@ -8,7 +8,7 @@ weight: -150
 author: Kacper Łukawski
 author_link: https://kacperlukawski.com
 date: 2024-07-25T00:00:00.000Z
-category: vector-search-manuals
+category: mastering-search
 ---
 It's been over a year since we published the original article on how to build a hybrid
@@ -7,7 +7,7 @@ social_preview_image: /articles_data/indexing-optimization/preview/social_previe
 weight: -155
 author: Sabrina Aquino  
 date: 2025-02-13T00:00:00.000Z  
-category: vector-search-manuals
+category: production-ops
 ---
 # Optimizing Memory Consumption During Bulk Uploads
@@ -18,7 +18,7 @@ keywords:
  - question answering
  - openai
  - embeddings
-category: practicle-examples
+category: demos-and-tutorials
 ---
 # Streamlining Question Answering: Simplifying Integration with LangChain and Qdrant
@@ -8,7 +8,7 @@ weight: -160
 author: Kacper Łukawski
 author_link: https://kacperlukawski.com
 date: 2024-08-14T00:00:00.000Z
-category: machine-learning
+category: embedding-research
 ---
 \* At least any open-source model, since you need access to its internals.
@@ -1,9 +0,0 @@
 ---
 title: Machine Learning
 short_description: "Machine learning articles on embeddings, metric learning, fine-tuning, and applying neural retrieval with Qdrant."
 description: Explore Machine Learning principles and practices which make modern semantic similarity search possible. Apply Qdrant and vector search capabilities to your ML projects.
 category: machine-learning
 url: /articles/machine-learning/
 isCategoryPage: true
 weight: 40
 ---
@@ -0,0 +1,9 @@
 ---
 title: Mastering Search
 short_description: "Hands-on guides to building better search with Qdrant: filtering, hybrid search, multivectors, and the Query API."
 description: Go beyond the basics and master vector search with Qdrant. Learn how to combine filtering, hybrid retrieval, multivectors, and reranking to build high-quality search.
 category: mastering-search
 url: /articles/mastering-search/
 isCategoryPage: true
 weight: 20
 ---
@@ -9,7 +9,7 @@ weight: 7
 author: Andrei Vasnetsov
 author_link: https://blog.vasnetsov.com/
 date: 2022-12-07T10:18:00.000Z
-category: qdrant-internals
+category: production-ops
 # aliases: [ /articles/memory-consumption/ ]
 ---
@@ -10,7 +10,7 @@ weight: 20
 author: Andrei Vasnetsov
 author_link: https://blog.vasnetsov.com/
 date: 2021-05-15T10:18:00.000Z
-category: machine-learning
+category: embedding-research
 # aliases: [ /articles/metric-learning-tips/ ]
 ---
@@ -13,7 +13,7 @@ keywords:
  - sparse retrieval
  - bm25
  - splade
-category: machine-learning
+category: embedding-research
 ---
 Have you ever heard of sparse neural retrieval? If so, have you used it in production? 
@@ -12,7 +12,7 @@ tags:
  - sparse retrieval
  - splade
  - bm25
-category: machine-learning
+category: embedding-research
 ---
 Finding enough time to study all the modern solutions while keeping your production running is rarely feasible.
@@ -14,7 +14,7 @@ keywords:
  - custom sharding
  - multiple partitions
  - vector database
-category: vector-search-manuals
+category: production-ops
 ---
 # Scaling Your Machine Learning Setup: The Power of Multitenancy and Custom Sharding in Qdrant 
@@ -7,7 +7,7 @@ social_preview_image: /articles_data/muvera-embeddings/preview/social_preview.jp
 author: Kacper Łukawski
 author_link: https://kacperlukawski.com
 date: 2025-09-05T00:00:00.000Z
-category: vector-search-manuals
+category: mastering-search
 ---
 ## What are MUVERA Embeddings?
@@ -10,7 +10,7 @@ weight: 50
 author: Andrey Vasnetsov
 author_link: https://blog.vasnetsov.com/
 date: 2021-06-10T10:18:00.000Z
-category: vector-search-manuals
+category: demos-and-tutorials
 # aliases: [ /articles/neural-search-tutorial/ ]
 ---
 # Neural Search 101: A Comprehensive Guide and Step-by-Step Tutorial
@@ -1,9 +0,0 @@
 ---
 title: Practical Examples
 short_description: "Hands-on examples that show how to build production search and RAG features using Qdrant and vector embeddings."
 description: Building blocks and reference implementations to help you get started with Qdrant. Learn how to use Qdrant to solve real-world problems and build the next generation of AI applications.
 category: practicle-examples
 url: /articles/practicle-examples/
 isCategoryPage: true
 weight: 60
 ---
@@ -14,7 +14,7 @@ keywords:
  - vector search
  - product quantization
  - memory optimization
-category: qdrant-internals
+category: production-ops
 aliases: [ /articles/product_quantization/ ]
 ---
@@ -0,0 +1,9 @@
 ---
 title: Production Ops
 short_description: "Running Qdrant in production: scaling, memory optimization, quantization, multitenancy, and access control."
 description: Operate Qdrant at scale. Learn how to optimize memory and resources, apply quantization, manage multitenancy and sharding, and secure access in production.
 category: production-ops
 url: /articles/production-ops/
 isCategoryPage: true
 weight: 40
 ---
@@ -16,7 +16,8 @@ keywords:
  - cohere
  - co.embed
  - embeddings
-category: practicle-examples
+category: demos-and-tutorials
 hideFromList: true
 ---
 Bi-encoders are probably the most efficient way of setting up a semantic Question Answering system. 
@@ -5,5 +5,5 @@ description: Take a look under the hood of Qdrant’s high-performance vector se
 category: qdrant-internals
 url: /articles/qdrant-internals/
 isCategoryPage: true
-weight: 20
+weight: 50
 ---
@@ -0,0 +1,9 @@
 ---
 title: RAG & Agents
 short_description: "Building RAG and agentic systems on Qdrant: agentic RAG, agent memory, semantic caching, and API access."
 description: Build retrieval-augmented generation and agentic applications with Qdrant. Learn agentic RAG patterns, agent memory, semantic caching, and how agents access your data.
 category: rag-and-agents
 url: /articles/rag-and-agents/
 isCategoryPage: true
 weight: 70
 ---
@@ -1,9 +0,0 @@
 ---
 title: RAG & GenAI
 short_description: "Articles on retrieval-augmented generation and GenAI patterns built on Qdrant, from agentic RAG to hybrid retrieval."
 description: Leverage Qdrant for Retrieval-Augmented Generation (RAG) and build AI Agents
 category: rag-and-genai
 url: /articles/rag-and-genai/
 isCategoryPage: true
 weight: 50
 ---
@@ -15,7 +15,7 @@ keywords:
  - vector search
  - retrieval augmented generation
  - gemini 1.5
-category: rag-and-genai
+category: core-concepts
 ---
 # Is RAG Dead? The Role of Vector Databases in AI Efficiency and Vector Search 
@@ -17,7 +17,7 @@ keywords:
 - quotient
 - optimization
 - rag
-category: rag-and-genai
+category: search-quality
 ---
 In today's fast-paced, information-rich world, AI is revolutionizing knowledge management. The systematic process of capturing, distributing, and effectively using knowledge within an organization is one of the fields in which AI provides exceptional value today. 
@@ -13,7 +13,7 @@ keywords:
  - reranking
  - query rewriting
  - relevance
-category: machine-learning
+category: search-quality
 ---
 A year ago, we dropped a statement-bomb in the “[Relevance Feedback in Information Retrieval](https://qdrant.tech/articles/search-feedback-loop/)” article and then went silent.
@@ -14,7 +14,7 @@ keywords:
  - vector search
  - scalar quantization
  - memory optimization
-category: qdrant-internals
+category: production-ops
 ---
 # Efficiency Unleashed: The Power of Scalar Quantization
@@ -11,7 +11,7 @@ author_link: https://llogiq.github.io
 date: 2023-08-14T00:00:00+01:00
 draft: false
 keywords: search, semantic, vector, llm, integration, benchmark, recommend, performance, rust
-category: practicle-examples
+category: demos-and-tutorials
 ---
 Qdrant is one of the fastest vector search engines out there, so while looking for a demo to show off, we came upon the idea to do a search-as-you-type box with a fully semantic search backend. Now we already have a semantic/keyword hybrid search on our website. But that one is written in Python, which incurs some overhead for the interpreter. Naturally, I wanted to see how fast I could go using Rust.
@@ -16,7 +16,7 @@ keywords:
    - lexical search
    - search
    - informational retrieval
-category: machine-learning
+category: search-quality
 ---
 > A problem well stated is a problem half solved.
@@ -0,0 +1,9 @@
 ---
 title: Search Quality
 short_description: "Measuring and improving search relevance with Qdrant: evaluation, relevance feedback, and benchmarking."
 description: Learn how to evaluate and improve the quality of your vector search. Explore relevance feedback, evaluation methodologies, and benchmarking techniques.
 category: search-quality
 url: /articles/search-quality/
 isCategoryPage: true
 weight: 30
 ---
@@ -18,7 +18,7 @@ tags:
  - AI applications
  - data retrieval
  - efficient data storage
-category: rag-and-genai
+category: rag-and-agents
 ---
 ## What is Semantic Cache?
@@ -11,7 +11,7 @@ author_link: https://llogiq.github.io
 date: 2023-07-12T10:00:00+01:00
 draft: false
 keywords: rust, serverless, lambda, semantic, search
-category: practicle-examples
+category: demos-and-tutorials
 ---
 Do you want to insert a semantic search function into your website or online app? Now you can do so - without spending any money! In this example, you will learn how to create a free prototype search engine for your own non-commercial purposes.
@@ -8,7 +8,7 @@ weight: -200
 author: Thierry Damiba
 author_link: https://github.com/thierrydamiba
 date: 2026-03-09T00:00:00.000Z
-category: practicle-examples
+category: mastering-search
 ---
 *This is Part 1 of a 5-part series on fine-tuning sparse embeddings for e-commerce search. We'll go from "why bother?" to a production system that beats BM25 by 29%.*
@@ -8,7 +8,7 @@ weight: -199
 author: Thierry Damiba
 author_link: https://github.com/thierrydamiba
 date: 2026-03-09T00:00:00.000Z
-category: practicle-examples
+category: mastering-search
 ---
 *This is Part 2 of a 5-part series on fine-tuning sparse embeddings for e-commerce search. In [Part 1](/articles/sparse-embeddings-ecommerce-part-1/), we covered why sparse embeddings beat BM25 for e-commerce. Now we build the training pipeline.*
@@ -8,7 +8,7 @@ weight: -198
 author: Thierry Damiba
 author_link: https://github.com/thierrydamiba
 date: 2026-03-09T00:00:00.000Z
-category: practicle-examples
+category: mastering-search
 ---
 *This is Part 3 of a 5-part series on fine-tuning sparse embeddings for e-commerce search. In [Part 2](/articles/sparse-embeddings-ecommerce-part-2/), we trained a SPLADE model on Modal. Now we evaluate it and push further with hard negative mining.*
@@ -8,7 +8,7 @@ weight: -197
 author: Thierry Damiba
 author_link: https://github.com/thierrydamiba
 date: 2026-03-09T00:00:00.000Z
-category: practicle-examples
+category: mastering-search
 ---
 *This is Part 4 of a 5-part series on fine-tuning sparse embeddings for e-commerce search. In [Part 3](/articles/sparse-embeddings-ecommerce-part-3/), we evaluated our model and implemented hard negative mining. Now we test how well it generalizes.*
@@ -8,7 +8,7 @@ weight: -196
 author: Thierry Damiba
 author_link: https://github.com/thierrydamiba
 date: 2026-03-09T00:00:00.000Z
-category: practicle-examples
+category: mastering-search
 ---
 *This is Part 5 of a series on fine-tuning sparse embeddings for e-commerce search. Parts [1](/articles/sparse-embeddings-ecommerce-part-1/)–[4](/articles/sparse-embeddings-ecommerce-part-4/) built the pipeline from scratch. This article packages it into a tool anyone can use.*
@@ -15,7 +15,7 @@ keywords:
  - SPLADE
  - hybrid search
  - vector search
-category: vector-search-manuals
+category: core-concepts
 ---
 Think of a library with a vast index card system. Each index card only has a few keywords marked out (sparse vector) of a large possible set for each book (document). This is what sparse vectors enable for text. 
@@ -14,7 +14,7 @@ tags:
  - Database
  - Search
  - Similarity Search
-category: vector-search-manuals
+category: mastering-search
 ---
 # How to Optimize Vector Storage by Storing Multiple Vectors Per Object
@@ -9,7 +9,7 @@ weight: 30
 author: Yusuf Sarıgöz
 author_link: https://medium.com/@yusufsarigoz
 date: 2022-03-24T15:12:00+03:00
-category: machine-learning
+category: embedding-research
 # aliases: [ /articles/triplet-loss/ ]
 ---
@@ -8,7 +8,7 @@ weight: -200
 author: Sabrina Aquino, David Myriel
 author_link: 
 date: 2024-09-10T00:00:00.000Z
-category: vector-search-manuals
+category: mastering-search
 ---
 Imagine you sell computer hardware. To help shoppers easily find products on your website, you need to have a **user-friendly [search engine](https://qdrant.tech)**.
@@ -1,9 +0,0 @@
 ---
 title: Vector Search Manuals
 short_description: "Practical manuals for vector search with Qdrant: indexing, filtering, hybrid retrieval, quantization, and scaling tips."
 description: Take full control of your vector data with Qdrant. Learn how to easily store, organize, and optimize vectors for high-performance similarity search.
 category: vector-search-manuals
 url: /articles/vector-search-manuals/
 isCategoryPage: true
 weight: 10
 ---
@@ -7,7 +7,7 @@ social_preview_image: /articles_data/vector-search-production/social_preview.png
 author: David Myriel
 author_link: 
 date: 2025-04-30T00:00:00.000Z
-category: vector-search-manuals
+category: production-ops
 ---
 ## What Does it Take to Run Search in Production?
@@ -6,7 +6,7 @@ preview_dir: /articles_data/vector-search-resource-optimization/preview
 social_preview_image: /articles_data/vector-search-resource-optimization/preview/social_preview.jpg
 weight: -200
 author: David Myriel
-category: vector-search-manuals
+category: production-ops
 date: 2025-02-09T00:00:00.000Z
 ---
@@ -17,7 +17,7 @@ keywords:
    - vector similarity
    - exploration
    - recommendation
-category: ecosystem
+hideFromList: true
 ---
@@ -18,7 +18,7 @@ tags:
  - embeddings
  - machine-learning
  - artificial intelligence
-category: vector-search-manuals
+category: core-concepts
 ---
 > **Embeddings** are numerical machine learning representations of the semantic of the input data. They capture the meaning of complex, high-dimensional data, like text, images, or audio, into vectors. Enabling algorithms to process and analyze the data more efficiently.
@@ -15,7 +15,7 @@ tags:
  - vector-search
  - vector-database
  - embeddings
-category: vector-search-manuals
+category: core-concepts
 ---
 ## An Introduction to Vector Databases
@@ -17,7 +17,7 @@ tags:
  - product quantization
  - scalar quantization
  - vector compression
-category: vector-search-manuals
+category: core-concepts
 ---
 Vector quantization is a data compression technique used to reduce the size of high-dimensional data. Compressing vectors reduces memory usage while maintaining nearly all of the essential information. This method allows for more efficient storage and faster search operations, particularly in large datasets.
@@ -18,7 +18,7 @@ tags:
  - embeddings
  - llm rag
  - rag application
-category: rag-and-genai
+category: core-concepts
 ---
 > Retrieval-augmented generation (RAG) integrates external information retrieval into the process of generating responses by Large Language Models (LLMs). It searches a database for information beyond its pre-trained knowledge base, significantly improving the accuracy and relevance of the generated responses.
@@ -1,11 +1,11 @@
 ---
 #Delimiter files are used to separate the list of documentation pages into sections.
 type: reference
-reference: /articles/vector-search-manuals
+reference: /articles/core-concepts
 weight: 110
 sitemapExclude: True
 build:
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 partition: learn
---
+---
@@ -2,7 +2,7 @@
 #Delimiter files are used to separate the list of documentation pages into sections.
 type: reference
 reference: /articles/data-exploration
-weight: 130
+weight: 180
 sitemapExclude: True
 build:
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@@ -0,0 +1,11 @@
 ---
 #Delimiter files are used to separate the list of documentation pages into sections.
 type: reference
 reference: /articles/demos-and-tutorials
 weight: 190
 sitemapExclude: True
 build:
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 partition: learn
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@@ -1,10 +0,0 @@
 ---
 type: reference
 reference: /articles/ecosystem
 weight: 170
 sitemapExclude: True
 build:
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 partition: learn
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@@ -0,0 +1,11 @@
 ---
 #Delimiter files are used to separate the list of documentation pages into sections.
 type: reference
 reference: /articles/embedding-research
 weight: 160
 sitemapExclude: True
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@@ -0,0 +1,11 @@
 ---
 #Delimiter files are used to separate the list of documentation pages into sections.
 type: reference
 reference: /articles/mastering-search
 weight: 120
 sitemapExclude: True
 build:
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@@ -1,10 +0,0 @@
 ---
 type: reference
 reference: /articles/practicle-examples
 weight: 160
 sitemapExclude: True
 build:
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 partition: learn
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@@ -1,11 +1,11 @@
 ---
 #Delimiter files are used to separate the list of documentation pages into sections.
 type: reference
-reference: /articles/machine-learning
+reference: /articles/production-ops
 weight: 140
 sitemapExclude: True
 build:
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---
+---
@@ -2,7 +2,7 @@
 #Delimiter files are used to separate the list of documentation pages into sections.
 type: reference
 reference: /articles/qdrant-internals
-weight: 120
+weight: 150
 sitemapExclude: True
 build:
  publishResources: false
@@ -1,11 +1,11 @@
 ---
 #Delimiter files are used to separate the list of documentation pages into sections.
 type: reference
-reference: /articles/rag-and-genai
+reference: /articles/rag-and-agents
-weight: 150
+weight: 170
 sitemapExclude: True
 build:
  publishResources: false
  render: never
 partition: learn
---
+---
@@ -0,0 +1,11 @@
 ---
 #Delimiter files are used to separate the list of documentation pages into sections.
 type: reference
 reference: /articles/search-quality
 weight: 130
 sitemapExclude: True
 build:
  publishResources: false
  render: never
 partition: learn
 ---
@@ -68,3 +68,10 @@
 /documentation/hybrid-search/   /documentation/search/hybrid-queries/    301
 /documentation/scroll/           /documentation/manage-data/points/#scroll-points       301
 /documentation/discovery/        /documentation/search/explore/           301
 # Articles category reorganization (technical articles taxonomy)
 /articles/vector-search-manuals/   /articles/mastering-search/      301
 /articles/machine-learning/        /articles/embedding-research/    301
 /articles/ecosystem/               /articles/demos-and-tutorials/   301
 /articles/practicle-examples/      /articles/demos-and-tutorials/   301
 /articles/rag-and-genai/           /articles/rag-and-agents/        301