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feat: vectorchord (#18042)

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* wip

auto-detect available extensions

auto-recovery, fix reindexing check

use original image for ml

* set probes

* update image for sql checker

update images for gha

* cascade

* fix new instance

* accurate dummy vector

* simplify dummy

* preexisiting pg docs

* handle different db name

* maybe fix sql generation

* revert refreshfaces sql change

* redundant switch

* outdated message

* update docker compose files

* Update docs/docs/administration/postgres-standalone.md

Co-authored-by: Daniel Dietzler <36593685+danieldietzler@users.noreply.github.com>

* tighten range

* avoid always printing "vector reindexing complete"

* remove nesting

* use new images

* add vchord to unit tests

* debug e2e image

* mention 1.107.2 in startup error

* support new vchord versions

---------

Co-authored-by: Daniel Dietzler <36593685+danieldietzler@users.noreply.github.com>
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docs/docs/features/searching.md
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@ -5,7 +5,7 @@ import TabItem from '@theme/TabItem';
Immich uses Postgres as its search database for both metadata and contextual CLIP search.
Contextual CLIP search is powered by the [pgvecto.rs](https://github.com/tensorchord/pgvecto.rs) extension, utilizing machine learning models like [CLIP](https://openai.com/research/clip) to provide relevant search results. This allows for freeform searches without requiring specific keywords in the image or video metadata.
Contextual CLIP search is powered by the [VectorChord](https://github.com/tensorchord/VectorChord) extension, utilizing machine learning models like [CLIP](https://openai.com/research/clip) to provide relevant search results. This allows for freeform searches without requiring specific keywords in the image or video metadata.
## Advanced Search Filters