Standardize on 'filterable' instead of 'filtrable'

2026-10-04 10:28:29 +02:00 · 2026-01-15 16:11:53 +01:00
parent ae335d93d8
commit 2d4ee38f37
44 changed files with 42 additions and 42 deletions
@@ -135,7 +135,7 @@ collection_info = client.get_collection(collection_name)
 print("Collection info:", collection_info)
 ```

-Expected output: Detailed collection information showing `points_count=2`, vector configuration, and [HNSW](https://qdrant.tech/articles/filtrable-hnsw/) settings.
+Expected output: Detailed collection information showing `points_count=2`, vector configuration, and [HNSW](https://qdrant.tech/articles/filterable-hnsw/) settings.

 ## Step 8: Run Your First Similarity Search

@@ -349,4 +349,4 @@ encoder_large = SentenceTransformer("all-mpnet-base-v2")  # Larger, potentially
 encoder_fast = SentenceTransformer("all-MiniLM-L12-v2")   # Different size/speed tradeoff
 ```

-**Ready for Day 2?** Tomorrow you'll learn how Qdrant makes vector search lightning-fast through [HNSW](https://qdrant.tech/articles/filtrable-hnsw/) indexing and how to optimize for production workloads.
+**Ready for Day 2?** Tomorrow you'll learn how Qdrant makes vector search lightning-fast through [HNSW](https://qdrant.tech/articles/filterable-hnsw/) indexing and how to optimize for production workloads.
@@ -9,7 +9,7 @@ weight: 30

 # Indexing and Performance

-Master [HNSW](https://qdrant.tech/articles/filtrable-hnsw/) indexing and practical tuning for fast retrieval.
+Master [HNSW](https://qdrant.tech/articles/filterable-hnsw/) indexing and practical tuning for fast retrieval.

 ---

@@ -8,7 +8,7 @@ weight: 4

 # Demo: HNSW Performance Tuning

-Learn how to improve vector search speed with [HNSW](https://qdrant.tech/articles/filtrable-hnsw/) tuning and payload indexing on a real 100K dataset.
+Learn how to improve vector search speed with [HNSW](https://qdrant.tech/articles/filterable-hnsw/) tuning and payload indexing on a real 100K dataset.

 **Follow along in Colab:** <a href="https://colab.research.google.com/github/qdrant/examples/blob/master/course/day_2/hnsw_performance_tuning.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" style="display:inline; margin:0;" alt="Open In Colab"/>
@@ -8,7 +8,7 @@ weight: 3

 # Combining Vector Search and Filtering

-We've talked about how Qdrant uses the [HNSW](/documentation/concepts/indexing/#filtrable-index) graph to efficiently search dense vectors. But in real-world applications, you'll often want to constrain your search using filters. This creates unique challenges for graph traversal that Qdrant solves elegantly.
+We've talked about how Qdrant uses the [HNSW](/documentation/concepts/indexing/#filterable-index) graph to efficiently search dense vectors. But in real-world applications, you'll often want to constrain your search using filters. This creates unique challenges for graph traversal that Qdrant solves elegantly.

 <div class="video">
 <iframe 
@@ -199,4 +199,4 @@ See more in [the docs](/documentation/concepts/filtering/).

 In the next section, we'll define a collection with structured payloads, configure payload indexing, and evaluate how different HNSW parameters impact filtered search performance.

-Learn more: [Filterable HNSW Article](https://qdrant.tech/articles/filtrable-hnsw/) 
+Learn more: [Filterable HNSW Article](https://qdrant.tech/articles/filterable-hnsw/) 
@@ -8,7 +8,7 @@ weight: 5

 # Project: HNSW Performance Benchmarking

-Now that you've seen how [HNSW](https://qdrant.tech/articles/filtrable-hnsw/) parameters and payload indexes affect performance with the DBpedia dataset, it's time to optimize for your own domain and use case.
+Now that you've seen how [HNSW](https://qdrant.tech/articles/filterable-hnsw/) parameters and payload indexes affect performance with the DBpedia dataset, it's time to optimize for your own domain and use case.

 ## Your Mission

@@ -25,7 +25,7 @@ At this point, you've learned how vector search retrieves the nearest vectors to

 You might wonder if Qdrant calculates the distance to every single vector in your collection for each query. This method, known as brute force search, technically works but with millions or billions of vectors this is too slow per query.

-Fortunately, Qdrant speeds things up with **[HNSW — Hierarchical Navigable Small World](https://qdrant.tech/articles/filtrable-hnsw/)**.
+Fortunately, Qdrant speeds things up with **[HNSW — Hierarchical Navigable Small World](https://qdrant.tech/articles/filterable-hnsw/)**.

 ### The Library Analogy