Standardize on 'filterable' instead of 'filtrable'

qdrant-landing/content/blog/case-study-nyris.md

@@ -46,7 +46,7 @@ With the emergence of pure-play, native vector search engines, Nyris conducted e
 
 As part of their selection process, Nyris evaluated several critical factors to ensure they chose the best vector search engine solution:
 
-- **Accuracy and Speed**: These were primary considerations. Nyris needed to understand the performance differences between the [HNSW](https://qdrant.tech/articles/filtrable-hnsw/) graph-based approach and brute-force search. In particular, they examined edge cases that required numerous filters, sometimes necessitating a switch to brute-force search. Even in these scenarios, Qdrant demonstrated impressive speed and reliability, meeting Nyris's stringent performance requirements.
+- **Accuracy and Speed**: These were primary considerations. Nyris needed to understand the performance differences between the [HNSW](https://qdrant.tech/articles/filterable-hnsw/) graph-based approach and brute-force search. In particular, they examined edge cases that required numerous filters, sometimes necessitating a switch to brute-force search. Even in these scenarios, Qdrant demonstrated impressive speed and reliability, meeting Nyris's stringent performance requirements.
 - **Insert Speed**: Nyris assessed how quickly data could be inserted into the database, including the performance during simultaneous data ingests and query requests. Qdrant excelled in this area, providing the necessary efficiency for their operations.
 - **Total Cost of Ownership**: Nyris analyzed the infrastructure costs and licensing fees associated with each solution. Qdrant offered a competitive total cost of ownership, making it an economically viable option.
 - **Data Sovereignty**: The ability to deploy Qdrant in their own clusters was a key aspect for Nyris, ensuring they maintained control over their data and complied with relevant data sovereignty requirements.

qdrant-landing/content/blog/qdrant-1.15.x.md

@@ -281,7 +281,7 @@ This modification, in combinations with [incremental HNSW indexing](/blog/qdrant
 
 ### HNSW Graph connectivity estimation
 
-Qdrant builds [addtitional HNSW links](/articles/filtrable-hnsw/) to ensure that filtered searches are performed fast and accurate.
+Qdrant builds [addtitional HNSW links](/articles/filterable-hnsw/) to ensure that filtered searches are performed fast and accurate.
 
 It does, however, introduce an overhead for indexing complexity, especially when the number of payload indexes is large.
 With v1.15, Qdrant introduces an optimization, which quickly estimates graph connectivity before creating additional links.

qdrant-landing/content/blog/qdrant-1.16.x.md

@@ -50,9 +50,9 @@ To use Tiered Multitenancy, after [setting up a collection with a shared fallbac
 
 ![Section 2](/blog/qdrant-1.16.x/section-2.png)
 
-To enhance the scalability and speed of vector search, Qdrant employs a graph-based index structure known as [HNSW (Hierarchical Navigable Small World)](/documentation/concepts/indexing/#vector-index). While traditional HNSW is primarily designed for unfiltered searches, Qdrant has addressed this limitation by implementing a [filterable HSNW index](/articles/filtrable-hnsw/). This innovative approach extends the HNSW graph with additional edges that correspond to indexed payload values. This enables Qdrant to maintain search quality even with high filtering selectivity, without introducing any runtime overhead during the search process.
+To enhance the scalability and speed of vector search, Qdrant employs a graph-based index structure known as [HNSW (Hierarchical Navigable Small World)](/documentation/concepts/indexing/#vector-index). While traditional HNSW is primarily designed for unfiltered searches, Qdrant has addressed this limitation by implementing a [filterable HSNW index](/articles/filterable-hnsw/). This innovative approach extends the HNSW graph with additional edges that correspond to indexed payload values. This enables Qdrant to maintain search quality even with high filtering selectivity, without introducing any runtime overhead during the search process.
 
-Even with filterable HNSW graphs, there are instances where the quality of search results can deteriorate significantly. This can happen when you use a combination of high cardinality filters, leading to the HNSW graph becoming [disconnected](/documentation/concepts/indexing/#filtrable-index). It is impractical to build additional links for every possible combination of filters in advance due to the potentially vast number of combinations. Another case where filterable HNSW may break down is in situations where filtering criteria are not known in advance.
+Even with filterable HNSW graphs, there are instances where the quality of search results can deteriorate significantly. This can happen when you use a combination of high cardinality filters, leading to the HNSW graph becoming [disconnected](/documentation/concepts/indexing/#filterable-index). It is impractical to build additional links for every possible combination of filters in advance due to the potentially vast number of combinations. Another case where filterable HNSW may break down is in situations where filtering criteria are not known in advance.
 
 To address these limitations, in version 1.16 we are introducing support for [ACORN](/documentation/concepts/search/#acorn-search-algorithm), based on the ACORN-1 algorithm described in the paper [ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data](https://arxiv.org/abs/2403.04871). With ACORN enabled, Qdrant not only traverses direct neighbors (the first hop) in the HNSW graph but also examines neighbors of neighbors (the second hop) if the direct neighbors have been filtered out. This enhancement improves search accuracy at the expense of performance, especially when multiple low-selectivity filters are applied.
 

qdrant-landing/content/blog/series-A-funding-round.md

@@ -27,7 +27,7 @@ The rise of generative AI in the last few years has shone a spotlight on vector
 
 ## What sets Qdrant apart?
 
-To meet the needs of the next generation of AI applications, Qdrant has always been built with four keys in mind: efficiency, scalability, performance, and flexibility. Our goal is to give our users unmatched speed and reliability, even when they are building massive-scale AI applications requiring the handling of billions of vectors. We did so by building Qdrant on Rust for performance, memory safety, and scale. Additionally, [our custom HNSW search algorithm](/articles/filtrable-hnsw/) and unique [filtering](/documentation/concepts/filtering/) capabilities consistently lead to [highest RPS](/benchmarks/), minimal latency, and high control with accuracy when running large-scale, high-dimensional operations. 
+To meet the needs of the next generation of AI applications, Qdrant has always been built with four keys in mind: efficiency, scalability, performance, and flexibility. Our goal is to give our users unmatched speed and reliability, even when they are building massive-scale AI applications requiring the handling of billions of vectors. We did so by building Qdrant on Rust for performance, memory safety, and scale. Additionally, [our custom HNSW search algorithm](/articles/filterable-hnsw/) and unique [filtering](/documentation/concepts/filtering/) capabilities consistently lead to [highest RPS](/benchmarks/), minimal latency, and high control with accuracy when running large-scale, high-dimensional operations. 
 
 Beyond performance, we provide our users with the most flexibility in cost savings and deployment options. A combination of cutting-edge efficiency features, like [built-in compression options](/documentation/guides/quantization/), [multitenancy](/documentation/guides/multiple-partitions/) and the ability to [offload data to disk](/documentation/concepts/storage/), dramatically reduce memory consumption. Committed to privacy and security, crucial for modern AI applications, Qdrant now also offers on-premise and hybrid SaaS solutions, meeting diverse enterprise needs in a data-sensitive world. This approach, coupled with our open-source foundation, builds trust and reliability with engineers and developers, making Qdrant a game-changer in the vector database domain.