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Merge pull request #2444 from qdrant/tech-article-reorg-2
Technical Article Reorganization - Stage 2
This commit is contained in:
kanungle
@@ -1,5 +1,5 @@
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---
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title: "Optimizing OpenAI Embeddings: Enhance Efficiency with Qdrant's Binary Quantization"
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title: "Enhance Search Efficiency with Binary Quantization"
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draft: false
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slug: binary-quantization-openai
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short_description: Use Qdrant's Binary Quantization to enhance OpenAI embeddings
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@@ -1,5 +1,5 @@
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---
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title: "Binary Quantization - Vector Search, 40x Faster "
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title: "Binary Quantization: 40x Faster Vector Search"
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short_description: "Binary Quantization is a newly introduced mechanism of reducing the memory footprint and increasing performance"
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description: "Binary Quantization is a newly introduced mechanism of reducing the memory footprint and increasing performance"
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social_preview_image: /articles_data/binary-quantization/social_preview.png
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@@ -1,5 +1,5 @@
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---
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title: "BM42: New Baseline for Hybrid Search"
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title: "BM42: Attention-Based Sparse Embeddings for Hybrid Search"
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short_description: "Introducing next evolutionary step in lexical search."
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description: "Introducing BM42 - a new sparse embedding approach, which combines the benefits of exact keyword search with the intelligence of transformers."
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social_preview_image: /articles_data/bm42/social-preview.jpg
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@@ -1,5 +1,5 @@
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---
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title: Fine Tuning Similar Cars Search
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title: "Fine Tuning Similar Cars Search"
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short_description: "How to use similarity learning to search for similar cars"
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description: Learn how to train a similarity model that can retrieve similar car images in novel categories.
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social_preview_image: /articles_data/cars-recognition/preview/social_preview.jpg
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@@ -12,7 +12,7 @@ date: 2022-06-28T13:00:00+03:00
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draft: false
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# aliases: [ /articles/cars-recognition/ ]
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category: embedding-research
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hideFromList: true
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hideFromList: false
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---
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Supervised classification is one of the most widely used training objectives in machine learning,
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@@ -1,5 +1,5 @@
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---
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title: Qdrant Summer of Code 2024 - ONNX Cross Encoders in Python
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title: "Qdrant Summer of Code 2024 - ONNX Cross Encoders in Python"
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short_description: QSoC 2024 ONNX Cross Encoders in Python
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description: A summary of my work and experience at Qdrant Summer of Code 2024.
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preview_dir: /articles_data/cross-encoder-integration-gsoc/preview
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@@ -1,5 +1,5 @@
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---
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title: " Data Privacy with Qdrant: Implementing Role-Based Access Control (RBAC)" #required
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title: "Data Privacy with Qdrant's Role-Based Access Control (RBAC)"
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short_description: "Secure Your Data with Qdrant: Implementing RBAC"
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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!
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social_preview_image: /articles_data/data-privacy/preview/social_preview.jpg # This image will be used in social media previews, should be 1200x630px. Required.
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---
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title: Finding errors in datasets with Similarity Search
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title: "Detecting Dataset Errors with Similarity Search"
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short_description: Finding errors datasets with distance-based methods
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description: Improving quality of text-and-images datasets on the online furniture marketplace example.
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preview_dir: /articles_data/dataset-quality/preview
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---
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title: "Vector Search as a dedicated service"
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title: "Do You Need Dedicated Vector Search?"
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short_description: "Why vector search requires to be a dedicated service."
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description: "Why vector search requires a dedicated service."
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social_preview_image: /articles_data/dedicated-service/social-preview.png
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---
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title: "Built for Vector Search"
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title: "Why Vector Search Needs a Dedicated Database"
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short_description: "Why add-on vector search looks good — until you actually use it."
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description: "Why add-on vector search looks good — until you actually use it."
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social_preview_image: /articles_data/dedicated-vector-search/preview/social_preview.jpg
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---
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title: Metric Learning for Anomaly Detection
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title: "Metric Learning for Anomaly Detection"
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short_description: "How to use metric learning to detect anomalies: quality assessment of coffee beans with just 200 labelled samples"
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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.
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social_preview_image: /articles_data/detecting-coffee-anomalies/preview/social_preview.jpg
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@@ -12,7 +12,7 @@ date: 2022-05-04T13:00:00+03:00
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draft: false
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category: embedding-research
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# aliases: [ /articles/detecting-coffee-anomalies/ ]
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hideFromList: true
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hideFromList: false
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---
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Anomaly detection is a thirsting yet challenging task that has numerous use cases across various industries.
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---
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title: Qdrant Summer of Code 2024 - WASM based Dimension Reduction
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title: "Qdrant Summer of Code 2024 - WASM based Dimension Reduction"
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short_description: QSOC'24 WASM based Dimension Reduction
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description: My journey as a Qdrant Summer of Code 2024 participant working on enhancing vector visualization using WebAssembly (WASM) based dimension reduction.
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preview_dir: /articles_data/dimension-reduction-qsoc/preview
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---
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title: "Discovery needs context"
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title: "Discovery Search in Qdrant"
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short_description: Discover points by constraining the vector space.
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description: Discovery Search, an innovative way to constrain the vector space in which a search is performed, relying only on vectors.
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social_preview_image: /articles_data/discovery-search/social_preview.jpg
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---
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title: "Distance-based data exploration"
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title: "Data Exploration with Qdrant's Distance Matrix API"
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short_description: "Efficient visualization and clusterization of high-dimensional data with Qdrant"
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description: "Explore your data under a new angle with Qdrant's tools for dimensionality reduction, clusterization, and visualization."
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social_preview_image: /articles_data/distance-based-exploration/social-preview.jpg
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---
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title: Layer Recycling and Fine-tuning Efficiency
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title: "Layer Recycling and Fine-tuning Efficiency"
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short_description: Tradeoff between speed and performance in layer recycling
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description: Learn when and how to use layer recycling to achieve different performance targets.
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preview_dir: /articles_data/embedding-recycling/preview
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@@ -12,7 +12,7 @@ date: 2022-08-23T13:00:00+03:00
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draft: false
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aliases: [ /articles/embedding-recycler/ ]
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category: embedding-research
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hideFromList: true
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hideFromList: false
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---
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A recent [paper](https://arxiv.org/abs/2207.04993)
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---
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title: Q&A with Similarity Learning
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title: "Q&A with Similarity Learning"
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short_description: A complete guide to building a Q&A system with similarity learning.
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description: A complete guide to building a Q&A system using Quaterion and SentenceTransformers.
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social_preview_image: /articles_data/faq-question-answering/preview/social_preview.jpg
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---
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title: Filterable HNSW
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title: "Filterable HNSW Without Recall Loss"
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short_description: How to make ANN search with custom filtering?
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description: How to make ANN search with custom filtering? Search in selected subsets without losing the results.
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# external_link: https://blog.vasnetsov.com/posts/categorical-hnsw/
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---
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title: Food Discovery Demo
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title: "Multimodal Food Search Demo"
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short_description: Feeling hungry? Find the perfect meal with Qdrant's multimodal semantic search.
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description: Feeling hungry? Find the perfect meal with Qdrant's multimodal semantic search.
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preview_dir: /articles_data/food-discovery-demo/preview
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---
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title: Google Summer of Code 2023 - Polygon Geo Filter for Qdrant Vector Database
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title: "Google Summer of Code 2023 - Polygon Geo Filter for Qdrant"
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short_description: Gsoc'23 Polygon Geo Filter for Qdrant Vector Database
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description: A Summary of my work and experience at Qdrant's Gsoc '23.
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preview_dir: /articles_data/geo-polygon-filter-gsoc/preview
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---
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title: "How to choose an embedding model"
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title: "How to Choose an Embedding Model: Evaluation & Tradeoffs"
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short_description: "There is no one-size-fits-all solution when it comes to embedding models. Learn how to choose the right one for your use case."
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description: "Building proper search requires selecting the right embedding model for your specific use case. This guide helps you navigate the selection process based on performance, cost, and other practical considerations."
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preview_dir: /articles_data/how-to-choose-an-embedding-model/preview
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---
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title: "Hybrid Search Revamped - Building with Qdrant's Query API"
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title: "Hybrid Search with Qdrant's Query API"
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short_description: "Merging different search methods to improve the search quality was never easier"
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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."
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preview_dir: /articles_data/hybrid-search/preview
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---
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title: "Qdrant Internals: Immutable Data Structures"
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title: "Immutable Data Structures in Qdrant"
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short_description: "Learn how immutable data structures improve vector search performance in Qdrant."
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description: "Learn how immutable data structures improve vector search performance in Qdrant."
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social_preview_image: /articles_data/immutable-data-structures/social_preview.png
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---
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title: "Optimizing Memory for Bulk Uploads"
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title: "Optimizing Qdrant Memory for Bulk Vector Uploads"
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short_description: "Best practices to optimize memory usage during high-volume vector ingestion in Qdrant, ensuring stable and efficient deployments."
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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."
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preview_dir: /articles_data/indexing-optimization/preview
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---
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title: "Qdrant under the hood: io_uring"
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title: "Faster Disk I/O for Vector Search Using io_uring"
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short_description: "The Linux io_uring API offers great performance in certain cases. Here's how Qdrant uses it!"
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description: "Slow disk decelerating your Qdrant deployment? Get on top of IO overhead with this one trick!"
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social_preview_image: /articles_data/io_uring/social_preview.png
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---
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title: "Any* Embedding Model Can Become a Late Interaction Model... If You Give It a Chance!"
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title: "Late Interaction Retrieval with Dense Token Embeddings"
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short_description: "Standard dense embedding models perform surprisingly well in late interaction scenarios."
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description: "We recently discovered that embedding models can become late interaction models & can perform surprisingly well in some scenarios. See what we learned here."
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preview_dir: /articles_data/late-interaction-models/preview
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---
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title: Minimal RAM you need to serve a million vectors
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title: "Minimal RAM to Serve 1M Vectors"
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short_description: How to properly measure RAM usage and optimize Qdrant for memory consumption.
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description: How to properly measure RAM usage and optimize Qdrant for memory consumption.
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social_preview_image: /articles_data/memory-consumption/preview/social_preview.jpg
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---
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title: Metric Learning Tips & Tricks
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title: "Metric Learning Tips & Tricks"
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short_description: How to train an object matching model and serve it in production.
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description: Practical recommendations on how to train a matching model and serve it in production. Even with no labeled data.
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# external_link: https://vasnetsov93.medium.com/metric-learning-tips-n-tricks-2e4cfee6b75b
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---
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title: "Product Quantization in Vector Search | Qdrant"
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title: "Product Quantization for Vector Search"
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short_description: "Vector search with low memory? Try out our brand-new Product Quantization!"
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description: "Discover product quantization in vector search technology. Learn how it optimizes storage and accelerates search processes for high-dimensional data."
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social_preview_image: /articles_data/product-quantization/social_preview.png
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---
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title: Question Answering as a Service with Cohere and Qdrant
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title: "Question Answering as a Service with Cohere and Qdrant"
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short_description: "End-to-end Question Answering system for the biomedical data with SaaS tools: Cohere co.embed API and Qdrant"
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description: "End-to-end Question Answering system for the biomedical data with SaaS tools: Cohere co.embed API and Qdrant"
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social_preview_image: /articles_data/qa-with-cohere-and-qdrant/social_preview.png
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---
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title: "Is RAG Dead? The Role of Vector Databases in Vector Search | Qdrant"
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title: "Is RAG Dead? Why Long Context Windows Don't Replace RAG"
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short_description: Learn how Qdrant’s vector database enhances enterprise AI with superior accuracy and cost-effectiveness.
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description: Uncover the necessity of vector databases for RAG and learn how Qdrant's vector database empowers enterprise AI with unmatched accuracy and cost-effectiveness.
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social_preview_image: /articles_data/rag-is-dead/preview/social_preview.jpg
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---
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title: "Scalar Quantization: Background, Practices & More | Qdrant"
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title: "Scalar Quantization for Vector Search"
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short_description: "Discover scalar quantization for optimized data storage and improved performance, including data compression benefits and efficiency enhancements."
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description: "Discover the efficiency of scalar quantization for optimized data storage and enhanced performance. Learn about its data compression benefits and efficiency improvements."
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social_preview_image: /articles_data/scalar-quantization/social_preview.png
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---
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title: Semantic Search As You Type
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title: "Semantic Search As You Type"
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short_description: "Instant search using Qdrant"
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description: To show off Qdrant's performance, we show how to do a quick search-as-you-type that will come back within a few milliseconds.
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social_preview_image: /articles_data/search-as-you-type/preview/social_preview.jpg
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---
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title: "Semantic Cache: Accelerating AI with Lightning-Fast Data Retrieval"
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title: "Semantic Caching for RAG: Cut LLM Cost and Latency"
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short_description: "Semantic Cache for Best Results and Optimization."
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description: "Semantic cache is reshaping AI applications by enabling rapid data retrieval. Discover how its implementation benefits your RAG setup."
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preview_dir: /articles_data/semantic-cache-ai-data-retrieval/preview
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---
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title: Serverless Semantic Search
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title: "Serverless Semantic Search"
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short_description: "Need to setup a server to offer semantic search? Think again!"
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description: "Create a serverless semantic search engine using nothing but Qdrant and free cloud services."
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social_preview_image: /articles_data/serverless/social_preview.png
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---
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title: "What is a Sparse Vector? How to Achieve Vector-based Hybrid Search"
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title: "Understanding SPLADE and Sparse Vectors"
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short_description: "Discover sparse vectors, their function, and significance in modern data processing, including methods like SPLADE for efficient use."
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description: "Learn what sparse vectors are, how they work, and their importance in modern data processing. Explore methods like SPLADE for creating and leveraging sparse vectors efficiently."
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social_preview_image: /articles_data/sparse-vectors/social_preview.png
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---
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title: Triplet Loss - Advanced Intro
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title: "Advanced Introduction to Triplet Loss"
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short_description: "What are the advantages of Triplet Loss and how to efficiently implement it?"
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description: "What are the advantages of Triplet Loss over Contrastive loss and how to efficiently implement it?"
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social_preview_image: /articles_data/triplet-loss/social_preview.jpg
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---
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title: "Vector Search in Production"
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title: "Vector Search in Production: Scaling, HA & Tuning Guide"
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short_description: "A comprehensive guide to running vector search in production environments"
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description: "We gathered our most recommended tips and tricks to make your production deployment run smoothly."
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preview_dir: /articles_data/vector-search-production/preview
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---
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title: "Vector Similarity: Going Beyond Full-Text Search | Qdrant"
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title: "Vector Similarity: Going Beyond Full-Text Search"
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short_description: Explore how vector similarity enhances data discovery beyond full-text search, including diversity sampling and more!
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description: Discover how vector similarity expands data exploration beyond full-text search. Explore diversity sampling and more for enhanced data discovery!
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preview_dir: /articles_data/vector-similarity-beyond-search/preview
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---
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title: Google Summer of Code 2023 - Web UI for Visualization and Exploration
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title: "Google Summer of Code 2023 - Web UI for Visualization and Exploration"
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short_description: Gsoc'23 Web UI for Visualization and Exploration
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description: My journey as a Google Summer of Code 2023 student working on the "Web UI for Visualization and Exploration" project for Qdrant.
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preview_dir: /articles_data/web-ui-gsoc/preview
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---
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title: "What are Vector Embeddings? - Revolutionize Your Search Experience"
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title: "Vector Embeddings Explained: How They Work in ML & Search"
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draft: false
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slug: what-are-embeddings?
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short_description: Explore the power of vector embeddings. Learn to use numerical machine learning representations to build a personalized Neural Search Service with Fastembed.
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---
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title: "What is RAG: Understanding Retrieval-Augmented Generation"
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title: "Understanding Retrieval-Augmented Generation (RAG)"
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draft: false
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slug: what-is-rag-in-ai?
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short_description: What is RAG?
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@@ -37,8 +37,8 @@ content:
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list:
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title: "Featured articles:"
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elements:
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- Distance-based data exploration
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- Built for Vector Search
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- Data Exploration with Qdrant's Distance Matrix API
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- Why Vector Search Needs a Dedicated Database
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- Semantic Search As You Type
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link:
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url: /articles/
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