added relevance feedback tutorial v1 (#2171)

* added relevance feedback tutorial v1

* Small edits

* Add link to tutorial from docs

---------

Co-authored-by: Abdon Pijpelink <abdon.pijpelink@qdrant.com>

Author: mrscoopers

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

@@ -197,6 +197,8 @@ The `a`, `b`, and `c` parameters of the [`naive` strategy](#naive-strategy) need
 
 <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>
 
+For a hands-on tutorial on determining the parameters, using them with the Relevance Feedback Query, and evaluating the results, check out [Relevance Feedback in Qdrant](/documentation/tutorials-search-engineering/using-relevance-feedback/).
+
 ### Naive Strategy
 
 For now, `naive` is the only available strategy.

qdrant-landing/content/documentation/headless/content/tutorials/search-engineering.md

@@ -2,6 +2,7 @@
 | :--- | :--- | :--- | :--- | :--- |
 | [Semantic Search Intro](/documentation/tutorials-search-engineering/neural-search/) | Deploy a search service for company descriptions. | <span class="pill">FastAPI</span> | 30m | <span class="text-green">Beginner</span> |
 | [Hybrid Search with FastEmbed](/documentation/tutorials-search-engineering/hybrid-search-fastembed/) | Combine dense and sparse search. | <span class="pill">FastAPI</span> | 20m | <span class="text-green">Beginner</span> |
+| [Relevance Feedback](/documentation/tutorials-search-engineering/using-relevance-feedback/) | Relevance Feedback Retrieval in Qdrant | <span class="pill">Python</span> | 30m | <span class="text-yellow">Intermediate</span> |
 | [Collaborative Filtering](/documentation/tutorials-search-engineering/collaborative-filtering/) | Collaborative filtering using sparse embeddings. | <span class="pill">Python</span> | 45m | <span class="text-yellow">Intermediate</span> |
 | [Multivector Document Retrieval](/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) | PDF RAG using ColPali and embedding pooling. | <span class="pill">Python</span> | 30m | <span class="text-yellow">Intermediate</span> |
 | [Retrieval Quality Evaluation](/documentation/tutorials-search-engineering/retrieval-quality/) | Measure quality and tune HNSW parameters. | <span class="pill">Python</span> | 30m | <span class="text-yellow">Intermediate</span> |