Manticore Search 28.4.4:

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.

Highlights

Faster KNN rescoring

KNN search now batches distance calculations during the rescore pass. 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 /search JSON API as well as SQL CALL 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 MODEL also gained custom_prompt support, 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://manticoresearch.com | sh

The 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:

curl https://manticoresearch.com | sh -s help

Facets can keep zero-count buckets visible

Faceted search now supports zero-count facet buckets through SQL ZEROES and 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 with max-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 for ranker and boolean_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_threads caps 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.