repo to PyPi

qdrant-landing/content/articles/relevance-feedback.md

@@ -419,7 +419,7 @@ For example, Relevance Feedback Query can be a great aid for search agents, lett
 
 ### How to Use It
 
-For ease of use, as one shouldn't need a machine learning degree to use a new feature, we published a [Python package that customizes Naive Formula weights for your dataset, retriever, and feedback model](https://github.com/qdrant/relevance-feedback).
+For ease of use, as one shouldn't need a machine learning degree to use a new feature, we published a [Python package that customizes Naive Formula weights for your dataset, retriever, and feedback model](https://pypi.org/project/qdrant-relevance-feedback/).
 
 What you need is a Qdrant collection, an idea of which feedback model you'd like to use to guide your retriever, and, optionally, a small set of use case-specific queries (50–300).
 
@@ -438,7 +438,7 @@ Once you've obtained the weights, simply plug them into your [Qdrant Client of c
 
 ### Evaluating Your Gains
 
-Additionally, the [Relevance Feedback Parameters package](https://github.com/qdrant/relevance-feedback) provides an `Evaluator` module with two metrics: **relative gain** based on the **abovethreshold@N** metric from the "Experiments" section above, and a metric more recognizable to people in search -- **Discounted Cumulative Gain (DCG) Win Rate**.
+Additionally, the [Relevance Feedback Parameters package](https://pypi.org/project/qdrant-relevance-feedback/) provides an `Evaluator` module with two metrics: **relative gain** based on the **abovethreshold@N** metric from the "Experiments" section above, and a metric more recognizable to people in search -- **Discounted Cumulative Gain (DCG) Win Rate**.
 
 - **Discounted Cumulative Gain (DCG) Win Rate**  
     For each query, we compute DCG@N for both compared methods (vanilla and relevance feedback-based retrieval) against ground truth relevancy scores from a feedback model. The method with the higher DCG@N gets a "win".

qdrant-landing/content/documentation/concepts/search-relevance.md

@@ -193,7 +193,7 @@ To leverage the feedback in search across the entire collection, Qdrant provides
 
 Internally, Qdrant combines the feedback list into pairs, based on the relevance scores, and then uses these pairs in a formula that modifies vector space traversal during retrieval (changes the strategy of retrieval). This relevance feedback-based retrieval considers not only the similarity of candidates to the query but also to each feedback pair. For a more detailed description of how it works, refer to the article [Relevance Feedback in Qdrant](/articles/relevance-feedback).
 
-The `a`, `b`, and `c` parameters of the [`naive` strategy](#naive-strategy) need to be customized for each triplet of retriever, feedback model, and collection. To get these 3 weights adapted to your setup, use [our open source Python package](https://github.com/qdrant/relevance-feedback).
+The `a`, `b`, and `c` parameters of the [`naive` strategy](#naive-strategy) need to be customized for each triplet of retriever, feedback model, and collection. To get these 3 weights adapted to your setup, use [our open source Python package](https://pypi.org/project/qdrant-relevance-feedback/).
 
 <aside role="alert">When using point IDs for <code>target</code> or <code>example</code>, these points are excluded from the search results. To include them, convert them to raw vectors first and use the raw vectors in the query.</aside>