From e5d910122735f6bd1b8294ade6e25909afa99318 Mon Sep 17 00:00:00 2001 From: Dylan Couzon Date: Wed, 22 Apr 2026 15:26:29 -0400 Subject: [PATCH] retrieval-quality: tone pass on intro, section rename, drop RAG link - Rewrite the intro so the ANN algorithm reads as one of several levers shaping retrieval quality (alongside the embedding model, retrieval strategy, filtering, reranking) rather than the only factor beyond embeddings. Addresses mrscoopers on the reductive "embeddings + ANN" framing. - Rename the "Retrieval Quality" section to "ANN Recall" and rewrite its opening paragraph to match; ANN approximation quality isn't the same as retrieval quality broadly. - Drop the RAG-evaluation-guide link from the three places it appeared in this file. This tutorial isn't RAG-specific. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../retrieval-quality.md | 20 ++++++------------- 1 file changed, 6 insertions(+), 14 deletions(-) diff --git a/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality.md b/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality.md index 65dd6f5f2..2038e297c 100644 --- a/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality.md +++ b/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality.md @@ -13,28 +13,20 @@ weight: 6 This tutorial measures **layer 1** of the evaluation ladder, **ANN recall**: the share of exact kNN results that Qdrant's approximate nearest-neighbor search recovers. For retrieval relevance (layer 2), see the Building a Golden Query Set tutorial. -Semantic search pipelines are as good as the embeddings they use. If your model cannot properly represent input data, similar objects might -be far away from each other in the vector space. No surprise, that the search results will be poor in this case. There is, however, another -component of the process which can also degrade the quality of the search results. It is the ANN algorithm itself. - -In this tutorial, we will show how to measure the quality of the semantic retrieval and how to tune the parameters of the HNSW, the ANN -algorithm used in Qdrant, to obtain the best results. +We'll measure Qdrant's ANN recall with `recall@k` and tune HNSW parameters to control the recall/latency trade-off. The ANN algorithm is one of several levers that shape retrieval quality in a production pipeline, alongside the embedding model, retrieval strategy (dense, sparse, hybrid, and multi-vector), filtering, and reranking. ## Embeddings Quality The quality of the embeddings is a topic for a separate tutorial. In a nutshell, it is usually measured and compared by benchmarks, such as [Massive Text Embedding Benchmark (MTEB)](https://huggingface.co/spaces/mteb/leaderboard). The evaluation process itself is pretty straightforward and is based on a ground truth dataset built by humans. We have a set of queries and a set of the documents we would expect -to receive for each of them. In the [evaluation process](https://qdrant.tech/rag/rag-evaluation-guide/), we take a query, find the most similar documents in the vector space and compare +to receive for each of them. In the evaluation process, we take a query, find the most similar documents in the vector space and compare them with the ground truth. In that setup, **finding the most similar documents is implemented as full kNN search, without any approximation**. As a result, we can measure the quality of the embeddings themselves, without the influence of the ANN algorithm. -## Retrieval Quality +## ANN Recall -Embeddings quality is indeed the most important factor in the semantic search quality. However, vector search engines, such as Qdrant, do not -perform pure kNN search. Instead, they use **Approximate Nearest Neighbors** (ANN) algorithms, which are much faster than the exact search, -but can return suboptimal results. We can also **measure the retrieval quality of that approximation** which also contributes to the overall -search quality. +The embedding model sets a baseline for search quality, but the retrieval pipeline can still underperform it. Vector search engines such as Qdrant don't run pure kNN at query time; they use **approximate nearest-neighbor** (ANN) algorithms for speed. ANN is faster than exact search but can return suboptimal results. **ANN recall** measures that gap. For a broader discussion of what to measure and when (ANN recall vs retrieval relevance vs business impact, and which metric fits which scenario), see [Retrieval Quality Fundamentals](/documentation/tutorials-search-engineering/retrieval-quality-fundamentals/). This tutorial focuses on the @@ -44,7 +36,7 @@ match the ANN-benchmarks convention. ## Measure the Quality of the Search Results -Let's build a quality [evaluation](https://qdrant.tech/rag/rag-evaluation-guide/) of the ANN algorithm in Qdrant. We will, first, call the search endpoint in a standard way to obtain +Let's build a quality evaluation of the ANN algorithm in Qdrant. We will, first, call the search endpoint in a standard way to obtain the approximate search results. Then, we will call the exact search endpoint to obtain the exact matches, and finally compare both results in terms of recall. @@ -212,7 +204,7 @@ to do it. ## Wrapping Up -Assessing the quality of retrieval is a critical aspect of [evaluating](https://qdrant.tech/rag/rag-evaluation-guide/) semantic search performance. It is imperative to measure retrieval quality when aiming for optimal quality of. +Assessing the quality of retrieval is a critical aspect of evaluating semantic search performance. It is imperative to measure retrieval quality when aiming for optimal quality of. your search results. Qdrant provides a built-in exact search mode, which can be used to measure the quality of the ANN algorithm itself, even in an automated way, as part of your CI/CD pipeline.