ERROR [ImmichMicroservices] [JobService] Unable to run job handler (smartSearch/smart-search): QueryFailedError: pgvecto.rs: The given vector is invalid for input

[Str1atum]

The bug

Brand new docker stack and database, trying to add around 700 files from external library, machine learning enabled (see docker files):

Microservices brings up lots of errors à la:

[Nest] 7 - 05/29/2024, 10:14:59 PM ERROR [ImmichMicroservices] [JobService] Unable to run job handler (smartSearch/smart-search): QueryFailedError: pgvecto.rs: The given vector is invalid for input. ADVICE: Check if dimensions and scalar type of the vector is matched with the index. [Nest] 7 - 05/29/2024, 10:14:59 PM ERROR [ImmichMicroservices] [JobService] QueryFailedError: pgvecto.rs: The given vector is invalid for input. ADVICE: Check if dimensions and scalar type of the vector is matched with the index. at PostgresQueryRunner.query (/usr/src/app/node_modules/typeorm/driver/postgres/PostgresQueryRunner.js:219:19) at process.processTicksAndRejections (node:internal/process/task_queues:95:5) at async InsertQueryBuilder.execute (/usr/src/app/node_modules/typeorm/query-builder/InsertQueryBuilder.js:106:33) at async SearchRepository.upsert (/usr/src/app/dist/repositories/search.repository.js:188:9) at async SmartInfoService.handleEncodeClip (/usr/src/app/dist/services/smart-info.service.js:91:9) at async /usr/src/app/dist/services/job.service.js:145:36 at async Worker.processJob (/usr/src/app/node_modules/bullmq/dist/cjs/classes/worker.js:394:28) at async Worker.retryIfFailed (/usr/src/app/node_modules/bullmq/dist/cjs/classes/worker.js:581:24) [Nest] 7 - 05/29/2024, 10:14:59 PM ERROR [ImmichMicroservices] [JobService] Object: { "id": "307c7eaf-8ac9-4a7a-b909-7bee74d5aaf1", "source": "upload" }

The OS that Immich Server is running on

Ubuntu 24.04 LTS

Version of Immich Server

1.105.1

Version of Immich Mobile App

v.105

Platform with the issue

• [X] Server
• [ ] Web
• [ ] Mobile

Your docker-compose.yml content

#
# WARNING: Make sure to use the docker-compose.yml of the current release:
#
# https://github.com/immich-app/immich/releases/latest/download/docker-compose.yml
#
# The compose file on main may not be compatible with the latest release.
#

name: immich

services:
  immich-server:
    container_name: immich_server
    image: ghcr.io/immich-app/immich-server:${IMMICH_VERSION:-release}
    command: ['start.sh', 'immich']
    volumes:
      - ${UPLOAD_LOCATION}:/usr/src/app/upload
      - /etc/localtime:/etc/localtime:ro
      - /mnt/somestorage:/diskstation-photos:ro
    env_file:
      - .env
    ports:
      - 2283:3001
    labels:
      - traefik.enable=true
      - traefik.http.routers.immich.rule=Host(`immich.mydomain.org`)
      - traefik.http.routers.immich.entrypoints=websecure
      - traefik.http.routers.immich.tls=true
      - traefik.http.routers.immich.tls.certresolver=letsencrypt
    depends_on:
      - redis
      - database
    restart: unless-stopped

  immich-microservices:
    container_name: immich_microservices
    image: ghcr.io/immich-app/immich-server:${IMMICH_VERSION:-release}
    extends: # uncomment this section for hardware acceleration - see https://immich.app/docs/features/hardware-transcoding
      file: hwaccel.transcoding.yml
      service: quicksync # set to one of [nvenc, quicksync, rkmpp, vaapi, vaapi-wsl] for accelerated transcoding
    command: ['start.sh', 'microservices']
    volumes:
      - ${UPLOAD_LOCATION}:/usr/src/app/upload
      - /etc/localtime:/etc/localtime:ro
      - /mnt/somestorage:/diskstation-photos:ro
    env_file:
      - .env
    depends_on:
      - redis
      - database
    restart: unless-stopped

  immich-machine-learning:
    container_name: immich_machine_learning
    # For hardware acceleration, add one of -[armnn, cuda, openvino] to the image tag.
    # Example tag: ${IMMICH_VERSION:-release}-cuda
    image: ghcr.io/immich-app/immich-machine-learning:${IMMICH_VERSION:-release}-openvino
    extends: # uncomment this section for hardware acceleration - see https://immich.app/docs/features/ml-hardware-acceleration
      file: hwaccel.ml.yml
      service: openvino # set to one of [armnn, cuda, openvino, openvino-wsl] for accelerated inference - use the `-wsl` version for WSL2 where applicable
    volumes:
      - model-cache:/cache
    env_file:
      - .env
    restart: unless-stopped

  redis:
    container_name: immich_redis
    image: registry.hub.docker.com/library/redis:6.2-alpine@sha256:84882e87b54734154586e5f8abd4dce69fe7311315e2fc6d67c29614c8de2672
    restart: unless-stopped

  database:
    container_name: immich_postgres
    image: registry.hub.docker.com/tensorchord/pgvecto-rs:pg14-v0.2.0@sha256:90724186f0a3517cf6914295b5ab410db9ce23190a2d9d0b9dd6463e3fa298f0
    environment:
      POSTGRES_PASSWORD: ${DB_PASSWORD}
      POSTGRES_USER: ${DB_USERNAME}
      POSTGRES_DB: ${DB_DATABASE_NAME}
      POSTGRES_INITDB_ARGS: '--data-checksums'
    volumes:
      - ${DB_DATA_LOCATION}:/var/lib/postgresql/data
    restart: always
    command: ["postgres", "-c" ,"shared_preload_libraries=vectors.so", "-c", 'search_path="$$user", public, vectors', "-c", "logging_collector=on", "-c", "max_wal_size=2GB", "-c", "shared_buffers=512MB", "-c", "wal_compression=on"]

volumes:
  model-cache:

networks:
  default:
    name: traefik
    external: true

version: "3.8"

# Configurations for hardware-accelerated transcoding

# If using Unraid or another platform that doesn't allow multiple Compose files,
# you can inline the config for a backend by copying its contents
# into the immich-microservices service in the docker-compose.yml file.

# See https://immich.app/docs/features/hardware-transcoding for more info on using hardware transcoding.

services:
  cpu: {}

  nvenc:
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities:
                - gpu
                - compute
                - video

  quicksync:
    devices:
      - /dev/dri:/dev/dri

  rkmpp:
    security_opt: # enables full access to /sys and /proc, still far better than privileged: true
      - systempaths=unconfined
      - apparmor=unconfined
    group_add:
      - video
    devices:
      - /dev/rga:/dev/rga
      - /dev/dri:/dev/dri
      - /dev/dma_heap:/dev/dma_heap
      - /dev/mpp_service:/dev/mpp_service
      #- /dev/mali0:/dev/mali0 # only required to enable OpenCL-accelerated HDR -> SDR tonemapping
    volumes:
      #- /etc/OpenCL:/etc/OpenCL:ro # only required to enable OpenCL-accelerated HDR -> SDR tonemapping
      #- /usr/lib/aarch64-linux-gnu/libmali.so.1:/usr/lib/aarch64-linux-gnu/libmali.so.1:ro # only required to enable OpenCL-accelerated HDR -> SDR tonemapping

  vaapi:
    devices:
      - /dev/dri:/dev/dri

  vaapi-wsl: # use this for VAAPI if you're running Immich in WSL2
    devices:
      - /dev/dri:/dev/dri
    volumes:
      - /usr/lib/wsl:/usr/lib/wsl
    environment:
      - LD_LIBRARY_PATH=/usr/lib/wsl/lib
      - LIBVA_DRIVER_NAME=d3d12

version: "3.8"

# Configurations for hardware-accelerated machine learning

# If using Unraid or another platform that doesn't allow multiple Compose files,
# you can inline the config for a backend by copying its contents 
# into the immich-machine-learning service in the docker-compose.yml file.

# See https://immich.app/docs/features/ml-hardware-acceleration for info on usage.

services:
  armnn:
    devices:
      - /dev/mali0:/dev/mali0
    volumes:
      - /lib/firmware/mali_csffw.bin:/lib/firmware/mali_csffw.bin:ro # Mali firmware for your chipset (not always required depending on the driver)
      - /usr/lib/libmali.so:/usr/lib/libmali.so:ro # Mali driver for your chipset (always required)

  cpu: {}

  cuda:
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities:
                - gpu

  openvino:
    device_cgroup_rules:
      - "c 189:* rmw"
    devices:
      - /dev/dri:/dev/dri
    volumes:
      - /dev/bus/usb:/dev/bus/usb
    environment:
      - NEOReadDebugKeys=1
      - OverrideGpuAddressSpace=48 

  openvino-wsl:
    devices:
      - /dev/dri:/dev/dri
      - /dev/dxg:/dev/dxg
    volumes:
      - /dev/bus/usb:/dev/bus/usb
      - /usr/lib/wsl:/usr/lib/wsl

Your .env content

# You can find documentation for all the supported env variables at https://immich.app/docs/install/environment-variables

# The location where your uploaded files are stored
UPLOAD_LOCATION=/immich-photos
# The location where your database files are stored
DB_DATA_LOCATION=/docker-storage/immich_db

# To set a timezone, uncomment the next line and change Etc/UTC to a TZ identifier from this list: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones#List
TZ=Europe/Berlin

# The Immich version to use. You can pin this to a specific version like "v1.71.0"
IMMICH_VERSION=release

# Connection secret for postgres. You should change it to a random password
DB_PASSWORD=postgres

# The values below this line do not need to be changed
###################################################################################
DB_USERNAME=postgres
DB_DATABASE_NAME=immich

IMMICH_LOG_LEVEL=debug

Reproduction steps

adding files from external library in base setup, new database

Relevant log output

Additional information

No response

[yu-jingrui]

I am encountering the same error in 1.111.0.

[hunmac9]

Same error here. Smart search still seems to work, but possibly missing files?

[cstmth]

I am encountering the very same issues. I am running on a Synology DS224+ (Intel Celeron J4125, 18 GB RAM).

[bdsoha]

Same here, on version 119.1.

[mertalev]

Can all of you confirm that you're using non-default CLIP models? If so, when did you change the model? You can check the files in the machine learning service's /cache/clip folder for the date when the model was downloaded.

[bdsoha]

$ ls -alh /cache/clip
drwxrwxrwx 6 1024 users 4.0K May 19 21:52 ViT-B-32__openai