From 300397b46711303936c7bdeee9950d4c6df6c457 Mon Sep 17 00:00:00 2001 From: Dylan Couzon Date: Mon, 11 May 2026 14:32:47 -0400 Subject: [PATCH] point hybrid search prerequisite at text search guide Replaces the FastEmbed tutorial link with the hybrid search section of the core Text Search guide, since FastEmbed is a satellite library. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../tutorials-search-engineering/multi-representation-search.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/documentation/tutorials-search-engineering/multi-representation-search.md b/qdrant-landing/content/documentation/tutorials-search-engineering/multi-representation-search.md index e8d0d1e73..a7855b306 100644 --- a/qdrant-landing/content/documentation/tutorials-search-engineering/multi-representation-search.md +++ b/qdrant-landing/content/documentation/tutorials-search-engineering/multi-representation-search.md @@ -14,7 +14,7 @@ A document is rarely well-represented by a single embedding. A paper has a title This tutorial builds retrieval that uses each representation deliberately: named vectors per representation, fused via the Query API, grouped back to the document level for presentation, with score boosting for ranking preferences. Each step targets a specific retrieval failure mode you'd hit in production. -This tutorial assumes you've built hybrid (dense plus sparse) search before, that you're comfortable with [named vectors](/documentation/manage-data/vectors/#named-vectors), the [Query API](/documentation/search/hybrid-queries/), Reciprocal Rank Fusion (RRF), and Best Matching 25 (BM25). If hybrid search is new, start with [Hybrid Search with FastEmbed](/documentation/tutorials-search-engineering/hybrid-search-fastembed/) first. +This tutorial assumes you've built hybrid (dense plus sparse) search before, that you're comfortable with [named vectors](/documentation/manage-data/vectors/#named-vectors), the [Query API](/documentation/search/hybrid-queries/), Reciprocal Rank Fusion (RRF), and Best Matching 25 (BM25). If hybrid search is new, start with the [hybrid search section of the Text Search guide](/documentation/search/text-search/#combining-semantic-and-lexical-search-with-hybrid-search) first. ## Setup