From 3dc511dff309f41ef4b611869f7698b70e8e82f2 Mon Sep 17 00:00:00 2001 From: Maddie Duhon Date: Fri, 20 Sep 2024 10:25:39 -0400 Subject: [PATCH] Update retrieval-quality.md added eval guide links --- .../content/documentation/tutorials/retrieval-quality.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/qdrant-landing/content/documentation/tutorials/retrieval-quality.md b/qdrant-landing/content/documentation/tutorials/retrieval-quality.md index fedfbb85c..307a315b3 100644 --- a/qdrant-landing/content/documentation/tutorials/retrieval-quality.md +++ b/qdrant-landing/content/documentation/tutorials/retrieval-quality.md @@ -20,7 +20,7 @@ algorithm used in Qdrant, to obtain the best results. 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, 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](https://qdrant.tech/rag/rag-evaluation-guide/), 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. @@ -50,7 +50,7 @@ algorithm approximates the exact search**. ## Measure the quality of the search results -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 +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 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 precision. @@ -218,7 +218,7 @@ to do it. ## Wrapping up -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. +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. 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.