Publish docs for version 1.17 (#2137)

* Cluster Telemetry docs (#2068) * mention cluster telemetry * small reword * Docs for read_fan_out_delay_ms * Add warning against setting threshold too low * Small docs change for inference API keys * Trigger Build * Add guide with tips for low latency search (#2136) * Update frontmatter weights * Add 'Tips for Low-Latency Search' guide * Review feedback * Temporarily bump Rust client to v1-17-upgrade branch * Docs for audit logging (#2141) * Docs for audit logging * Consistent title casing * Review feedback * Docs for optimization monitoring (#2121) * Docs for optimization monitoring * Review feedback * Optimization monitoring is cluster-wide now * Upgrade code snippet checker to 1.17 * Fix broken Python snippets * Relevance Feedback docs (#2060) * add relevance feedback in Explore page * Review * Trigger Build * Trigger Build * Create new 'Search Relevance' concept page * Tweaks * Update links to moved content * add rust snippet * TS anippets * Clarification about using point IDs * Add links * Restructure paragraphs * docs: Go snippet Signed-off-by: Anush008 <mail@anush.sh> * docs: Missed Java snippets with C# Signed-off-by: Anush008 <mail@anush.sh> * new: add python snippets * Make Java and Rust snippets testable * Remove unnecessary styling --------- Signed-off-by: Anush008 <mail@anush.sh> Co-authored-by: Evgeniya Sukhodolskaya <suxodolskaya97@gmail.com> Co-authored-by: Abdon Pijpelink <abdon.pijpelink@qdrant.com> Co-authored-by: Ivan Pleshkov <pleshkov.ivan@gmail.com> Co-authored-by: Anush008 <mail@anush.sh> Co-authored-by: George Panchuk <george.panchuk@qdrant.tech> * Docs for enable_hnsw (#2080) * Move 'Filterable HNSW Index' section under 'Vector Index' * Add docs for enable_hnsw * docs: Go snippets Signed-off-by: Anush008 <mail@anush.sh> * docs: Java snippets Signed-off-by: Anush008 <mail@anush.sh> * docs: C# snippets Signed-off-by: Anush008 <mail@anush.sh> * Add Rust snippet * TS snippets * new: add python snippets --------- Signed-off-by: Anush008 <mail@anush.sh> Co-authored-by: Anush008 <mail@anush.sh> Co-authored-by: timvisee <tim@visee.me> Co-authored-by: Ivan Pleshkov <pleshkov.ivan@gmail.com> Co-authored-by: George Panchuk <george.panchuk@qdrant.tech> * Docs for list shard keys API (#2082) * Docs for list shard keys * docs: Go snippets Signed-off-by: Anush008 <mail@anush.sh> * docs: Java snippets Signed-off-by: Anush008 <mail@anush.sh> * docs: C# snippets Signed-off-by: Anush008 <mail@anush.sh> * Add Rust snippet * docs: TS snippets * new: add python snippets --------- Signed-off-by: Anush008 <mail@anush.sh> Co-authored-by: Anush008 <mail@anush.sh> Co-authored-by: timvisee <tim@visee.me> Co-authored-by: Ivan Pleshkov <pleshkov.ivan@gmail.com> Co-authored-by: George Panchuk <george.panchuk@qdrant.tech> * Docs for update_mode (#2097) * Docs for update_mode * docs: C#, Go, Java snippets Signed-off-by: Anush008 <mail@anush.sh> * doc: Remove _ from C# snippet Signed-off-by: Anush008 <mail@anush.sh> * Add Rust snippet * Fix some snippets * Review feedback * ts snippets * new: add python snippets * fix: add generated python.md --------- Signed-off-by: Anush008 <mail@anush.sh> Co-authored-by: Anush008 <mail@anush.sh> Co-authored-by: timvisee <tim@visee.me> Co-authored-by: Ivan Pleshkov <pleshkov.ivan@gmail.com> Co-authored-by: George Panchuk <george.panchuk@qdrant.tech> * Docs for weighted RRF (#2132) * Docs for weighted RRF * docs: Go, Java, C# snippets Signed-off-by: Anush008 <mail@anush.sh> * Add Rust snippet * Apply suggestions from code review Co-authored-by: Luis Cossío <luis.cossio@qdrant.com> * Delete landing_page.sln * Review feedback * ts snippets * new: add python snippets * Trigger Build --------- Signed-off-by: Anush008 <mail@anush.sh> Co-authored-by: Anush008 <mail@anush.sh> Co-authored-by: timvisee <tim@visee.me> Co-authored-by: Luis Cossío <luis.cossio@qdrant.com> Co-authored-by: Ivan Pleshkov <pleshkov.ivan@gmail.com> Co-authored-by: George Panchuk <george.panchuk@qdrant.tech> * Clean up Go and Java snippets * Revert go snippet change; target Go client 1.17.1 * Updated Go snippet * Trigger Build * Update Rust lockfile --------- Signed-off-by: Anush008 <mail@anush.sh> Co-authored-by: Luis Cossío <luis.cossio@qdrant.com> Co-authored-by: Daniel Boros <56868953+dancixx@users.noreply.github.com> Co-authored-by: timvisee <tim@visee.me> Co-authored-by: Evgeniya Sukhodolskaya <suxodolskaya97@gmail.com> Co-authored-by: Ivan Pleshkov <pleshkov.ivan@gmail.com> Co-authored-by: Anush008 <mail@anush.sh> Co-authored-by: George Panchuk <george.panchuk@qdrant.tech>
2026-10-07 11:58:31 +02:00 · 2026-02-20 10:42:12 +01:00
parent 92ff228b79
commit 378837432e
113 changed files with 1942 additions and 256 deletions
@@ -213,7 +213,7 @@ The above will match:

 ## MMR Reranking

-We introduce [Maximal Marginal Relevance (MMR)](/documentation/concepts/hybrid-queries/#maximal-marginal-relevance-mmr) reranking to balance relevance and diversity.
+We introduce [Maximal Marginal Relevance (MMR)](/documentation/concepts/search-relevance/#maximal-marginal-relevance-mmr) reranking to balance relevance and diversity.
 MMR works by selecting the results iteratively, by picking the item with the best combination of similarity to the query and dissimilarity to the already selected items.

 It prevents your top-k results from being redundant and helps surface varied but relevant answers, particularly in dense datasets with overlapping entries.
@@ -225,7 +225,7 @@ It prevents your top-k results from being redundant and helps surface varied but
 Let’s say you’re building a knowledge assistant or semantic document explorer in which a single query can return multiple highly similar queries.
 For instance, searching “climate change” in a scientific paper database might return several similar paragraphs.

-You can diversify the results with [Maximal Marginal Relevance (MMR)](/documentation/concepts/hybrid-queries/#maximal-marginal-relevance-mmr).
+You can diversify the results with [Maximal Marginal Relevance (MMR)](/documentation/concepts/search-relevance/#maximal-marginal-relevance-mmr).

 Instead of returning the top-k results based on pure similarity, MMR helps select a diverse subset of high-quality results.
 This gives more coverage and avoids redundant results, which is helpful in dense content domains such as academic papers, product catalogs, or search assistants.