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Update retrieval-quality.md

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added eval guide links
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Maddie Duhon
2024-09-20 10:25:39 -04:00
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@@ -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 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 [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 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**. 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. 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 ## 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 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. in terms of precision.
@@ -218,7 +218,7 @@ to do it.
## Wrapping up ## 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, 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. even in an automated way, as part of your CI/CD pipeline.