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Manticore Search 28.4.4: Faster KNN, better conversational search, easier installs and more faceting controls #4945
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Originally posted by Sergey Nikolaev on https://mnt.cr/go/KlA41c on July 10, 2026
Manticore Search 28.4.4: Faster KNN, better conversational search, easier installs and more faceting controls
Manticore Search 28.4.4 speeds up KNN rescoring, adds HTTP/custom-prompt conversational search, simplifies installs, improves facets and relevance defaults, and fixes auth, replication, SQL, and columnar/KNN issues.
Manticore Search 28.4.4 has been released. This release brings faster KNN rescoring, more flexible conversational search, a simpler install and upgrade path, better faceting controls, per-table relevance defaults, and fixes across authentication, replication, SQL compatibility, distributed queries, and columnar/KNN internals.
This post is a catch-up for everything shipped from 27.2.0 through 28.4.4.
Upgrade Notes
Please review these before upgrading:
SPH_UDF_VERSIONto 12. External UDF, ranker, and token-filter plugin binaries must be rebuilt before loading them into this version. The change lets token-filter plugins receive longdict=keywords_32ktokens instead of silently bypassing values above the old 126-byte plugin limit. Existing table data and configuration remain compatible, and no index migration is required. Downgrade is possible if you also restore plugin binaries built for the older ABI and do not rely on the new long-token plugin behavior. (Issue #4667, PR #4668)embeddings_threadssetting, and several columnar/KNN build, packaging, and cleanup fixes. Mixing an older library with a newer daemon is not recommended. (PR #169, PR #186, PR #4676)Highlights
Faster KNN rescoring
KNN search now batches distance calculations during the
rescorepass. After HNSW returns the candidate set, Manticore recomputes final full-precision distances and re-sorts the results. Batching that work reduces per-candidate overhead in the final stage of vector search.For vector-heavy workloads, this takes work out of the part of the query that runs after candidate selection. Results do not change; the final ranking pass just has less overhead when many candidates are rescored.
Conversational search through SQL and HTTP
Conversational search is now available through the
/searchJSON API as well as SQLCALL CHAT. That makes it easier to use Manticore's chat flow from applications that already talk to the HTTP API and do not want to add a separate SQL path just for chat requests.CREATE CHAT MODELalso gainedcustom_promptsupport, so answers can follow application-specific instructions such as citation rules, tone, response length, or formatting. The feature is still built on the same Manticore Search flow: retrieve relevant documents from an existing vectorized table, build context, keep conversation history, and return an answer with supporting sources.One-line installation
The quick-start install path is now simpler:
curl https://mnt.cr/go/Do1DH6 | shThe same installer can also upgrade an existing installation, list available versions, switch between stable and development repositories, and install a selected version. Package managers still remain the source of truth for installed files, repositories, services, and dependencies; the new path just removes the manual setup steps around them.
For all options, run:
Facets can keep zero-count buckets visible
Faceted search now supports zero-count facet buckets through SQL
ZEROESand JSON"zeroes": true.This is a small but important UI feature. In e-commerce-style filtering, you often want to keep an option visible even when the current filter combination gives it a count of
0. Combined withmax-mode facet behavior, zero-count buckets make it easier to show selected, available, and currently unavailable choices without hiding part of the filter vocabulary from the user.Better defaults for search relevance
Manticore now supports
CREATE TABLE ... profile='relevance', plus stored per-table defaults forrankerandboolean_mode.Based on our search quality tests,
profile='relevance'and the ranking settings it enables improve relevance in many cases. The application also no longer needs to repeat the same ranking parameters in every request.More control over embedding CPU usage
embeddings_threadscaps the CPU threads used for auto-embedding inserts,ALTER TABLE ... REBUILD KNN, and text-to-vector KNN queries.This matters on shared hosts and mixed workloads. Embedding generation and KNN rebuilds can be CPU-heavy; a server-level cap makes those jobs easier to schedule without letting them take over the whole machine.
Bug Fixes
This release includes 17 bug fixes. The most important ones are:
COUNT(DISTINCT ...).GETFIELDfetch errors or malformed replies instead of returning apparently successful rows with empty or untrusted stored-field values..tmp.spc.*files from breaking later table rename, attach, or drop operations.SELECTqueries.SETstatements no longer fail under auth.здоров'ямmatchздоров'яunderlemmatize_uk_all.blend_mode, restoring separator-stripped variants consistently for indexing and keyword extraction.percentilesaggregations together with terms aggregations in the same/searchrequest on multi-chunk RT tables was fixed.For the complete list, see the changelog.
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