krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-10-11 22:08:31 +02:00
Code Issues Packages Projects Releases Wiki Activity

fix more links

Browse Source
This commit is contained in:
kanungle
2026-03-16 22:43:25 -07:00
parent 3a71dee188
commit f4d9d38a82
2 changed files with 3 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/course/essentials/day-2/filterable-hnsw.md
+1 -1
View File
@@ -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. 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/filterable-hnsw/) Learn more: [Filterable HNSW Article](/articles/filterable-hnsw/)
qdrant-landing/content/course/essentials/day-2/what-is-hnsw.md
+2 -2
View File
@@ -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. 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 Worlds](https://qdrant.tech/articles/filterable-hnsw/)**. Fortunately, Qdrant speeds things up with **[HNSW — Hierarchical Navigable Small Worlds](/articles/filterable-hnsw/)**.
### The Library Analogy ### The Library Analogy
@@ -335,6 +335,6 @@ For very tight RAM budgets consider these solutions:
Now you understand how HNSW makes vector search fast and scalable. Next we'll combine fast search with complex filters using Qdrant’s filter‑aware HNSW. Now you understand how HNSW makes vector search fast and scalable. Next we'll combine fast search with complex filters using Qdrant’s filter‑aware HNSW.
Learn more: [HNSW in Qdrant Documentation](https://qdrant.tech/documentation/concepts/indexing/#vector-index) Learn more: [HNSW in Qdrant Documentation](/documentation/concepts/indexing/#vector-index)
Ready to see how HNSW handles real-world filtering scenarios? Let's continue! Ready to see how HNSW handles real-world filtering scenarios? Let's continue!