From cf78f6e44b83885f2d0e491eabdd1248492ea395 Mon Sep 17 00:00:00 2001 From: Evgeniya Sukhodolskaya Date: Fri, 5 Jun 2026 11:44:05 +0200 Subject: [PATCH] Remove posting_list.rs links from SPLADE series parts 1 & 3 Co-Authored-By: Claude Sonnet 4.6 --- .../content/articles/sparse-embeddings-ecommerce-part-1.md | 2 +- .../content/articles/sparse-embeddings-ecommerce-part-3.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-1.md b/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-1.md index 4cce4fe90..2abc7a9a5 100644 --- a/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-1.md +++ b/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-1.md @@ -132,7 +132,7 @@ client.query_points( ) ``` -**Production-ready scaling.** Rust + [SIMD-optimized inverted index](https://github.com/qdrant/qdrant/blob/master/lib/sparse/src/index/posting_list.rs) with an on-disk option keeps RAM low even with 200+ active terms per doc across millions of products. +**Production-ready scaling.** Rust + SIMD-optimized inverted index with an on-disk option keeps RAM low even with 200+ active terms per doc across millions of products. **No ANN approximation.** Sparse retrieval uses an [inverted index](https://qdrant.tech/articles/sparse-vectors/), the same data structure powering BM25. Results are exact - no recall tradeoffs from approximate nearest neighbor search. diff --git a/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-3.md b/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-3.md index 2a181a8ce..444b375c7 100644 --- a/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-3.md +++ b/qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-3.md @@ -242,7 +242,7 @@ A common concern: isn't running a transformer on every query slow? | Sparse retrieval (Qdrant) | <1ms | Negligible | | **Total** | **10-20ms** | Real-time | -The retrieval itself is negligible. Qdrant's Rust + [SIMD-optimized inverted index](https://github.com/qdrant/qdrant/blob/master/lib/sparse/src/index/posting_list.rs) scans millions of posting lists in sub-millisecond time. All the latency is in the encoder, which runs once per query regardless of catalog size. +The retrieval itself is negligible. Qdrant's Rust + SIMD-optimized inverted index scans millions of posting lists in sub-millisecond time. All the latency is in the encoder, which runs once per query regardless of catalog size. Optimization strategies if 15ms isn't fast enough: - **Batch queries**: Encode multiple queries together (autocomplete, related searches)