image:
  # registry where weaviate image is stored
  registry: docker.io
  # Tag of weaviate image to deploy
  # Note: We strongly recommend you overwrite this value in your own values.yaml.
  # Otherwise a mere upgrade of the chart could lead to an unexpected upgrade
  # of weaviate. In accordance with Infra-as-code, you should pin this value
  # down and only change it if you explicitly want to upgrade the Weaviate
  # version.
  tag: 1.18.0
  repo: semitechnologies/weaviate

# overwrite command and args if you want to run specific startup scripts, for
# example setting the nofile limit
command: ["/bin/weaviate"]
args:
  - '--host'
  - '0.0.0.0'
  - '--port'
  - '8080'
  - '--scheme'
  - 'http'
  - '--config-file'
  - '/weaviate-config/conf.yaml'
  - --read-timeout=60s 
  - --write-timeout=60s

# below is an example that can be used to set an arbitrary nofile limit at
# startup:
#
# command: 
#   - "/bin/sh"
# args: 
#   - "-c"
#   - "ulimit -n 65535 && /bin/weaviate --host 0.0.0.0 --port 8080 --scheme http --config-file /weaviate-config/conf.yaml"

# Scale replicas of Weaviate. Note that as of v1.8.0 dynamic scaling is limited
# to cases where no data is imported yet. Scaling down after importing data may
# break usability. Full dynamic scalability will be added in a future release.
replicas: 1
resources: {}
  # requests:
  #   cpu: '500m'
  #   memory: '300Mi'
  # limits:
  #   cpu: '1000m'
  #   memory: '1Gi'


# Add a service account ot the Weaviate pods if you need Weaviate to have permissions to
# access kubernetes resources or cloud provider resources. For example for it to have
# access to a backup up bucket, or if you want to restrict Weaviate pod in any way.
# By default, use the default ServiceAccount
serviceAccountName:

# The Persistent Volume Claim settings for Weaviate. If there's a
# storage.fullnameOverride field set, then the default pvc will not be
# created, instead the one defined in fullnameOverride will be used
storage:
  size: 32Gi
  storageClassName: ""

# The service controls how weaviate is exposed to the outside world. If you
# don't want a public load balancer, you can also choose 'ClusterIP' to make
# weaviate only accessible within your cluster.
service:
  name: weaviate
  ports:
    - name: http
      protocol: TCP
      port: 80
      # Target port is going to be the same for every port
  type: LoadBalancer
  loadBalancerSourceRanges: []
  # optionally set cluster IP if you want to set a static IP
  clusterIP:
  annotations: {}

# Adjust liveness, readiness and startup probes configuration
startupProbe:
  # For kubernetes versions prior to 1.18 startupProbe is not supported thus can be disabled.
  enabled: false

  initialDelaySeconds: 300
  periodSeconds: 60
  failureThreshold: 50
  successThreshold: 1
  timeoutSeconds: 3

livenessProbe:
  initialDelaySeconds: 900
  periodSeconds: 10
  failureThreshold: 30
  successThreshold: 1
  timeoutSeconds: 3

readinessProbe:
  initialDelaySeconds: 3
  periodSeconds: 10
  failureThreshold: 3
  successThreshold: 1
  timeoutSeconds: 3


terminationGracePeriodSeconds: 600

# Weaviate Config
#
# The following settings allow you to customize Weaviate to your needs, for
# example set authentication and authorization options. See weaviate docs
# (https://www.weaviate.io/developers/weaviate/) for all
# configuration.
authentication:
  anonymous_access:
    enabled: true
authorization:
  admin_list:
    enabled: false
query_defaults:
  limit: 100
debug: false


# Insert any custom environment variables or envSecrets by putting the exact name
# and desired value into the settings below. Any env name passed will be automatically
# set for the statefulSet.
env:
  CLUSTER_GOSSIP_BIND_PORT: 7000
  CLUSTER_DATA_BIND_PORT: 7001
  # The aggressiveness of the Go Garbage Collector. 100 is the default value.
  GOGC: 100

  # Expose metrics on port 2112 for Prometheus to scrape
  PROMETHEUS_MONITORING_ENABLED: false

  # Set a MEM limit for the Weaviate Pod so it can help you both increase GC-related 
  # performance as well as avoid GC-related out-of-memory (“OOM”) situations
  # GOMEMLIMIT: 6GiB

  # Maximum results Weaviate can query with/without pagination
  # NOTE: Affects performance, do NOT set to a very high value.
  # The default is 100K
  QUERY_MAXIMUM_RESULTS: 100000

  # whether to enable vector dimensions tracking metric
  TRACK_VECTOR_DIMENSIONS: false

  # whether to re-index/-compute the vector dimensions metric (needed if upgrading from weaviate < v1.16.0)
  REINDEX_VECTOR_DIMENSIONS_AT_STARTUP: false

envSecrets:


# Configure backup providers
backups:
  # The backup-filesystem module enables creation of the DB backups in
  # the local filesystem
  filesystem:
    enabled: false
    envconfig:
      # Configure folder where backups should be saved
      BACKUP_FILESYSTEM_PATH: /tmp/backups
  
  s3:
    enabled: false
    # If one is using AWS EKS and has already configured K8s Service Account
    # that holds the AWS credentials one can pass a name of that service account
    # here using this setting.
    # NOTE: the root `serviceAccountName` config has priority over this one, and 
    # if the root one is set this one will NOT overwrite it. This one is here for
    # backwards compatibility.
    serviceAccountName:

    envconfig:
      # Configure bucket where backups should be saved, this setting is mandatory
      BACKUP_S3_BUCKET: weaviate-backups

      # Optional setting. Defaults to empty string. 
      # Set this option if you want to save backups to a given location
      # inside the bucket
      # BACKUP_S3_PATH: path/inside/bucket

      # Optional setting. Defaults to AWS S3 (s3.amazonaws.com). 
      # Set this option if you have a MinIO storage configured in your environment
      # and want to use it instead of the AWS S3.
      # BACKUP_S3_ENDPOINT: custom.minio.endpoint.address

      # Optional setting. Defaults to true. 
      # Set this option if you don't want to use SSL.
      # BACKUP_S3_USE_SSL: true

      # You can pass environment AWS settings here:
      # Define the region
      # AWS_REGION: eu-west-1

    # For Weaviate to be able to create bucket objects it needs a user credentials to authenticate to AWS.
    # The User must have permissions to read/create/delete bucket objects.
    # You can pass the User credentials (access-key id and access-secret-key) in 2 ways:
    # 1. by setting the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY plain values in the `secrets` section below
    #     this chart will create a kubernetes secret for you with these key-values pairs
    # 2. create Kubernetes secret/s with AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY  keys and their respective values 
    #     Set the Key and the secret where it is set in `envSecrets` section below
    secrets: {}
    #   AWS_ACCESS_KEY_ID: access-key-id (plain text)
    #   AWS_SECRET_ACCESS_KEY: secret-access-key (plain text)

    # If one has already defined secrets with AWS credentials one can pass them using
    # this setting:
    envSecrets: {}
    #   AWS_ACCESS_KEY_ID: name-of-the-k8s-secret-containing-the-key-id
    #   AWS_SECRET_ACCESS_KEY: name-of-the-k8s-secret-containing-the-key

  gcs:
    enabled: false
    envconfig:
      # Configure bucket where backups should be saved, this setting is mandatory
      BACKUP_GCS_BUCKET: weaviate-backups

      # Optional setting. Defaults to empty string.
      # Set this option if you want to save backups to a given location
      # inside the bucket
      # BACKUP_GCS_PATH: path/inside/bucket

      # You can pass environment Google settings here:
      # Define the project
      # GOOGLE_CLOUD_PROJECT: project-id

    # For Weaviate to be able to create bucket objects it needs a ServiceAccount credentials to authenticate to GCP.
    # The ServiceAccount must have permissions to read/create/delete bucket objects.
    # You can pass the ServiceAccount credentials (as JSON) in 2 ways:
    # 1. by setting the GOOGLE_APPLICATION_CREDENTIALS json as plain text in the `secrets` section below
    #     this chart will create a kubernetes secret for you with this key-values pairs
    # 2. create a Kubernetes secret with GOOGLE_APPLICATION_CREDENTIALS key and its respective value
    #     Set the Key and the secret where it is set in `envSecrets` section below
    secrets: {}
    #   GOOGLE_APPLICATION_CREDENTIALS: credentials-json-string

    # If one has already defined a secret with GOOGLE_APPLICATION_CREDENTIALS one can pass them using
    # this setting:
    envSecrets: {}
    #   GOOGLE_APPLICATION_CREDENTIALS: name-of-the-k8s-secret-containing-the-key

  azure:
    enabled: false
    envconfig:
      # Configure container where backups should be saved, this setting is mandatory
      BACKUP_AZURE_CONTAINER: weaviate-backups

      # Optional setting. Defaults to empty string. 
      # Set this option if you want to save backups to a given location
      # inside the container
      # BACKUP_AZURE_PATH: path/inside/container

    # For Weaviate to be able to create container objects it needs a user credentials to authenticate to Azure Storage.
    # The User must have permissions to read/create/delete container objects.
    # You can pass the User credentials (account-name id and account-key or connection-string) in 2 ways:
    # 1. by setting the AZURE_STORAGE_ACCOUNT and AZURE_STORAGE_KEY
    #     or AZURE_STORAGE_CONNECTION_STRING plain values in the `secrets` section below
    #     this chart will create a kubernetes secret for you with these key-values pairs
    # 2. create Kubernetes secret/s with AZURE_STORAGE_ACCOUNT and AZURE_STORAGE_KEY 
    #     or AZURE_STORAGE_CONNECTION_STRING and their respective values
    #     Set the Key and the secret where it is set in `envSecrets` section below
    secrets: {}
    #   AZURE_STORAGE_ACCOUNT: account-name (plain text)
    #   AZURE_STORAGE_KEY: account-key (plain text)
    #   AZURE_STORAGE_CONNECTION_STRING: connection-string (plain text)

    # If one has already defined secrets with Azure Storage credentials one can pass them using
    # this setting:
    envSecrets: {}
    #   AZURE_STORAGE_ACCOUNT: name-of-the-k8s-secret-containing-the-account-name
    #   AZURE_STORAGE_KEY: name-of-the-k8s-secret-containing-account-key
    #   AZURE_STORAGE_CONNECTION_STRING: name-of-the-k8s-secret-containing-connection-string


# modules are extensions to Weaviate, they can be used to support various
# ML-models, but also other features unrelated to model inference.
# An inference/vectorizer module is not required, you can also run without any
# modules and import your own vectors.
modules:

  # The text2vec-contextionary module uses a fastText-based vector-space to
  # derive vector embeddings for your objects. It is very efficient on CPUs,
  # but in some situations it cannot reach the same level of accuracy as
  # transformers-based models.
  text2vec-contextionary:
    # disable if you want to use transformers or import or own vectors
    enabled: false

    # The configuration below is ignored if enabled==false
    fullnameOverride: contextionary
    tag: en0.16.0-v1.0.2
    repo: semitechnologies/contextionary
    registry: docker.io
    replicas: 1
    envconfig:
      occurrence_weight_linear_factor: 0.75
      neighbor_occurrence_ignore_percentile: 5
      enable_compound_splitting: false
      extensions_storage_mode: weaviate
    resources:
      requests:
        cpu: '500m'
        memory: '500Mi'
      limits:
        cpu: '1000m'
        memory: '5000Mi'

    # It is possible to add a ServiceAccount to this module's Pods, it can be
    # used in cases where the module is in a private registry and you want to
    # give access to the registry only to this pod.
    # NOTE: if not set the root `serviceAccountName` config will be used.
    serviceAccountName:

    # You can guide where the pods are scheduled on a per-module basis,
    # as well as for Weaviate overall. Each module accepts nodeSelector,
    # tolerations, and affinity configuration. If it is set on a per-
    # module basis, this configuration overrides the global config.

    nodeSelector:
    tolerations:
    affinity:

  # The text2vec-transformers modules uses neural networks, such as BERT,
  # DistilBERT, etc. to dynamically compute vector embeddings based on the
  # sentence's context. It is very slow on CPUs and should run with
  # CUDA-enabled GPUs for optimal performance.
  text2vec-transformers:

    # enable if you want to use transformers instead of the
    # text2vec-contextionary module
    enabled: false
    # You can set directly an inference URL of this module without deploying it with this release.
    # You can do so by setting a value for the `inferenceUrl` here AND by setting the `enable` to `false`
    inferenceUrl: {}
    # The configuration below is ignored if enabled==false

    # replace with model of choice, see
    # https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/text2vec-transformers
    # for all supported models or build your own container.
    tag: distilbert-base-uncased
    repo: semitechnologies/transformers-inference
    registry: docker.io
    replicas: 1
    fullnameOverride: transformers-inference
    # Deprecated setting use initialDelaySeconds instead in each probe instead
    # probeInitialDelaySeconds: 120
    livenessProbe:
      initialDelaySeconds: 120
      periodSeconds: 3
      timeoutSeconds: 3
    readinessProbe:
      initialDelaySeconds: 120
      periodSeconds: 3
    envconfig:
      # enable for CUDA support. Your K8s cluster needs to be configured
      # accordingly and you need to explicitly set GPU requests & limits below
      enable_cuda: false

      # only used when cuda is enabled
      nvidia_visible_devices: all
      nvidia_driver_capabilities: compute,utility

      # only used when cuda is enabled
      ld_library_path: /usr/local/nvidia/lib64

    resources:
      requests:
        cpu: '1000m'
        memory: '3000Mi'

        # enable if running with CUDA support
        # nvidia.com/gpu: 1
      limits:
        cpu: '1000m'
        memory: '5000Mi'

        # enable if running with CUDA support
        # nvidia.com/gpu: 1
    
    # It is possible to add a ServiceAccount to this module's Pods, it can be
    # used in cases where the module is in a private registry and you want to
    # give access to the registry only to this pod.
    # NOTE: if not set the root `serviceAccountName` config will be used.
    serviceAccountName:

    # You can guide where the pods are scheduled on a per-module basis,
    # as well as for Weaviate overall. Each module accepts nodeSelector,
    # tolerations, and affinity configuration. If it is set on a per-
    # module basis, this configuration overrides the global config.

    nodeSelector:
    tolerations:
    affinity:

    passageQueryServices:
      passage:
        enabled: false
        # You can set directly an inference URL of this module without deploying it with this release.
        # You can do so by setting a value for the `inferenceUrl` here AND by setting the `enable` to `false`
        inferenceUrl: {}

        tag: facebook-dpr-ctx_encoder-single-nq-base
        repo: semitechnologies/transformers-inference
        registry: docker.io
        replicas: 1
        fullnameOverride: transformers-inference-passage
        livenessProbe:
          initialDelaySeconds: 120
          periodSeconds: 3
          timeoutSeconds: 3
        readinessProbe:
          initialDelaySeconds: 120
          periodSeconds: 3
        envconfig:
          # enable for CUDA support. Your K8s cluster needs to be configured
          # accordingly and you need to explicitly set GPU requests & limits below
          enable_cuda: false

          # only used when cuda is enabled
          nvidia_visible_devices: all
          nvidia_driver_capabilities: compute,utility

          # only used when cuda is enabled
          ld_library_path: /usr/local/nvidia/lib64

        resources:
          requests:
            cpu: '1000m'
            memory: '3000Mi'

            # enable if running with CUDA support
            # nvidia.com/gpu: 1
          limits:
            cpu: '1000m'
            memory: '5000Mi'

            # enable if running with CUDA support
            # nvidia.com/gpu: 1
        
        # You can guide where the pods are scheduled on a per-module basis,
        # as well as for Weaviate overall. Each module accepts nodeSelector,
        # tolerations, and affinity configuration. If it is set on a per-
        # module basis, this configuration overrides the global config.

        nodeSelector:
        tolerations:
        affinity:

      query:
        enabled: false
        # You can set directly an inference URL of this module without deploying it with this release.
        # You can do so by setting a value for the `inferenceUrl` here AND by setting the `enable` to `false`
        inferenceUrl: {}

        tag: facebook-dpr-question_encoder-single-nq-base
        repo: semitechnologies/transformers-inference
        registry: docker.io
        replicas: 1
        fullnameOverride: transformers-inference-query
        livenessProbe:
          initialDelaySeconds: 120
          periodSeconds: 3
          timeoutSeconds: 3
        readinessProbe:
          initialDelaySeconds: 120
          periodSeconds: 3
        envconfig:
          # enable for CUDA support. Your K8s cluster needs to be configured
          # accordingly and you need to explicitly set GPU requests & limits below
          enable_cuda: false

          # only used when cuda is enabled
          nvidia_visible_devices: all
          nvidia_driver_capabilities: compute,utility

          # only used when cuda is enabled
          ld_library_path: /usr/local/nvidia/lib64

        resources:
          requests:
            cpu: '1000m'
            memory: '3000Mi'

            # enable if running with CUDA support
            # nvidia.com/gpu: 1
          limits:
            cpu: '1000m'
            memory: '5000Mi'

            # enable if running with CUDA support
            # nvidia.com/gpu: 1
        
        # You can guide where the pods are scheduled on a per-module basis,
        # as well as for Weaviate overall. Each module accepts nodeSelector,
        # tolerations, and affinity configuration. If it is set on a per-
        # module basis, this configuration overrides the global config.

        nodeSelector:
        tolerations:
        affinity:

  # The text2vec-openai module uses OpenAI Embeddings API
  # to dynamically compute vector embeddings based on the
  # sentence's context.
  # More information about OpenAI Embeddings API can be found here:
  # https://beta.openai.com/docs/guides/embeddings/what-are-embeddings
  text2vec-openai:

    # enable if you want to use OpenAI module
    enabled: false

    # Set your OpenAI API Key to be passed to Weaviate pod as
    # an environment variable
    apiKey: ''

  # The text2vec-huggingface module uses HuggingFace API
  # to dynamically compute vector embeddings based on the
  # sentence's context.
  # More information about HuggingFace API can be found here:
  # https://huggingface.co/docs/api-inference/detailed_parameters#feature-extraction-task
  text2vec-huggingface:

    # enable if you want to use HuggingFace module
    enabled: false

    # Set your HuggingFace API Key to be passed to Weaviate pod as
    # an environment variable
    apiKey: ''

  # The text2vec-cohere module uses Cohere API
  # to dynamically compute vector embeddings based on the
  # sentence's context.
  # More information about Cohere API can be found here: https://docs.cohere.ai/
  text2vec-cohere:

    # enable if you want to use Cohere module
    enabled: false

    # Set your Cohere API Key to be passed to Weaviate pod as
    # an environment variable
    apiKey: ''

  # The ref2vec-centroid module
  ref2vec-centroid:

    # enable if you want to use Centroid module
    enabled: false

  # The multi2vec-clip modules uses CLIP transformers to vectorize both images
  # and text in the same vector space. It is typically slow(er) on CPUs and should
  # run with CUDA-enabled GPUs for optimal performance.
  multi2vec-clip:

    # enable if you want to use transformers instead of the
    # text2vec-contextionary module
    enabled: false
    # You can set directly an inference URL of this module without deploying it with this release.
    # You can do so by setting a value for the `inferenceUrl` here AND by setting the `enable` to `false`
    inferenceUrl: {}

    # The configuration below is ignored if enabled==false

    # replace with model of choice, see
    # https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/multi2vec-clip
    # for all supported models or build your own container.
    tag: sentence-transformers-clip-ViT-B-32-multilingual-v1
    repo: semitechnologies/multi2vec-clip
    registry: docker.io
    replicas: 1
    fullnameOverride: clip-inference
    livenessProbe:
      initialDelaySeconds: 120
      periodSeconds: 3
      timeoutSeconds: 3
    readinessProbe:
      initialDelaySeconds: 120
      periodSeconds: 3
    envconfig:
      # enable for CUDA support. Your K8s cluster needs to be configured
      # accordingly and you need to explicitly set GPU requests & limits below
      enable_cuda: false

      # only used when cuda is enabled
      nvidia_visible_devices: all
      nvidia_driver_capabilities: compute,utility

      # only used when cuda is enabled
      ld_library_path: /usr/local/nvidia/lib64

    resources:
      requests:
        cpu: '1000m'
        memory: '3000Mi'

        # enable if running with CUDA support
        # nvidia.com/gpu: 1
      limits:
        cpu: '1000m'
        memory: '5000Mi'

        # enable if running with CUDA support
        # nvidia.com/gpu: 1
    annotations:
    nodeSelector:
    tolerations:

  # The qna-transformers module uses neural networks, such as BERT,
  # DistilBERT, to find an aswer in text to a given question
  qna-transformers:
    enabled: false
    # You can set directly an inference URL of this module without deploying it with this release.
    # You can do so by setting a value for the `inferenceUrl` here AND by setting the `enable` to `false`
    inferenceUrl: {}
    tag: bert-large-uncased-whole-word-masking-finetuned-squad-34d66b1
    repo: semitechnologies/qna-transformers
    registry: docker.io
    replicas: 1
    fullnameOverride: qna-transformers
    livenessProbe:
      initialDelaySeconds: 120
      periodSeconds: 3
      timeoutSeconds: 3
    readinessProbe:
      initialDelaySeconds: 120
      periodSeconds: 3
    envconfig:
      # enable for CUDA support. Your K8s cluster needs to be configured
      # accordingly and you need to explicitly set GPU requests & limits below
      enable_cuda: false

      # only used when cuda is enabled
      nvidia_visible_devices: all
      nvidia_driver_capabilities: compute,utility

      # only used when cuda is enabled
      ld_library_path: /usr/local/nvidia/lib64

    resources:
      requests:
        cpu: '1000m'
        memory: '3000Mi'

        # enable if running with CUDA support
        # nvidia.com/gpu: 1
      limits:
        cpu: '1000m'
        memory: '5000Mi'

        # enable if running with CUDA support
        # nvidia.com/gpu: 1
    
    # It is possible to add a ServiceAccount to this module's Pods, it can be
    # used in cases where the module is in a private registry and you want to
    # give access to the registry only to this pod.
    # NOTE: if not set the root `serviceAccountName` config will be used.
    serviceAccountName:

    # You can guide where the pods are scheduled on a per-module basis,
    # as well as for Weaviate overall. Each module accepts nodeSelector,
    # tolerations, and affinity configuration. If it is set on a per-
    # module basis, this configuration overrides the global config.

    nodeSelector:
    tolerations:
    affinity:

  # The qna-openai module uses OpenAI Completions API
  # to dynamically answer given questions.
  # More information about OpenAI Completions API can be found here:
  # https://beta.openai.com/docs/api-reference/completions
  qna-openai:

    # enable if you want to use OpenAI module
    enabled: false

    # Set your OpenAI API Key to be passed to Weaviate pod as
    # an environment variable
    apiKey: ''

  # The generative-openai module uses OpenAI Completions API
  # along with text-davinci-003 model to behave as ChatGPT.
  # More information about OpenAI Completions API can be found here:
  # https://beta.openai.com/docs/api-reference/completions
  generative-openai:

    # enable if you want to use OpenAI module
    enabled: false

    # Set your OpenAI API Key to be passed to Weaviate pod as
    # an environment variable
    apiKey: ''

  # The img2vec-neural module uses neural networks, to generate
  # a vector representation of the image
  img2vec-neural:
    enabled: false
    # You can set directly an inference URL of this module without deploying it with this release.
    # You can do so by setting a value for the `inferenceUrl` here AND by setting the `enable` to `false`
    inferenceUrl: {}
    tag: resnet50
    repo: semitechnologies/img2vec-pytorch
    registry: docker.io
    replicas: 1
    fullnameOverride: img2vec-neural
    livenessProbe:
      initialDelaySeconds: 120
      periodSeconds: 3
      timeoutSeconds: 3
    readinessProbe:
      initialDelaySeconds: 120
      periodSeconds: 3
    envconfig:
      # enable for CUDA support. Your K8s cluster needs to be configured
      # accordingly and you need to explicitly set GPU requests & limits below
      enable_cuda: false

      # only used when cuda is enabled
      nvidia_visible_devices: all
      nvidia_driver_capabilities: compute,utility

      # only used when cuda is enabled
      ld_library_path: /usr/local/nvidia/lib64

    resources:
      requests:
        cpu: '1000m'
        memory: '3000Mi'

        # enable if running with CUDA support
        # nvidia.com/gpu: 1
      limits:
        cpu: '1000m'
        memory: '5000Mi'

        # enable if running with CUDA support
        # nvidia.com/gpu: 1
    
    # It is possible to add a ServiceAccount to this module's Pods, it can be
    # used in cases where the module is in a private registry and you want to
    # give access to the registry only to this pod.
    # NOTE: if not set the root `serviceAccountName` config will be used.
    serviceAccountName:

    # You can guide where the pods are scheduled on a per-module basis,
    # as well as for Weaviate overall. Each module accepts nodeSelector,
    # tolerations, and affinity configuration. If it is set on a per-
    # module basis, this configuration overrides the global config.

    nodeSelector:
    tolerations:
    affinity:

  # The text-spellcheck module uses spellchecker library to check
  # misspellings in a given text
  text-spellcheck:
    enabled: false
    # You can set directly an inference URL of this module without deploying it with this release.
    # You can do so by setting a value for the `inferenceUrl` here AND by setting the `enable` to `false`
    inferenceUrl: {}
    tag: pyspellchecker-en
    repo: semitechnologies/text-spellcheck-model
    registry: docker.io
    replicas: 1
    fullnameOverride: text-spellcheck
    livenessProbe:
      initialDelaySeconds: 120
      periodSeconds: 3
      timeoutSeconds: 3
    readinessProbe:
      initialDelaySeconds: 120
      periodSeconds: 3

    resources:
      requests:
        cpu: '400m'
        memory: '400Mi'
      limits:
        cpu: '500m'
        memory: '500Mi'
    
    # It is possible to add a ServiceAccount to this module's Pods, it can be
    # used in cases where the module is in a private registry and you want to
    # give access to the registry only to this pod.
    # NOTE: if not set the root `serviceAccountName` config will be used.
    serviceAccountName:

    # You can guide where the pods are scheduled on a per-module basis,
    # as well as for Weaviate overall. Each module accepts nodeSelector,
    # tolerations, and affinity configuration. If it is set on a per-
    # module basis, this configuration overrides the global config.

    nodeSelector:
    tolerations:
    affinity:

  # The ner-transformers module uses spellchecker library to check
  # misspellings in a given text
  ner-transformers:
    enabled: false
    # You can set directly an inference URL of this module without deploying it with this release.
    # You can do so by setting a value for the `inferenceUrl` here AND by setting the `enable` to `false`
    inferenceUrl: {}
    tag: dbmdz-bert-large-cased-finetuned-conll03-english-0.0.2
    repo: semitechnologies/ner-transformers
    registry: docker.io
    replicas: 1
    fullnameOverride: ner-transformers
    livenessProbe:
      initialDelaySeconds: 120
      periodSeconds: 3
      timeoutSeconds: 3
    readinessProbe:
      initialDelaySeconds: 120
      periodSeconds: 3
    envconfig:
      # enable for CUDA support. Your K8s cluster needs to be configured
      # accordingly and you need to explicitly set GPU requests & limits below
      enable_cuda: false

      # only used when cuda is enabled
      nvidia_visible_devices: all
      nvidia_driver_capabilities: compute,utility

      # only used when cuda is enabled
      ld_library_path: /usr/local/nvidia/lib64

    resources:
      requests:
        cpu: '1000m'
        memory: '3000Mi'

        # enable if running with CUDA support
        # nvidia.com/gpu: 1
      limits:
        cpu: '1000m'
        memory: '5000Mi'

        # enable if running with CUDA support
        # nvidia.com/gpu: 1
    
    # It is possible to add a ServiceAccount to this module's Pods, it can be
    # used in cases where the module is in a private registry and you want to
    # give access to the registry only to this pod.
    # NOTE: if not set the root `serviceAccountName` config will be used.
    serviceAccountName:

    # You can guide where the pods are scheduled on a per-module basis,
    # as well as for Weaviate overall. Each module accepts nodeSelector,
    # tolerations, and affinity configuration. If it is set on a per-
    # module basis, this configuration overrides the global config.

    nodeSelector:
    tolerations:
    affinity:

  # The sum-transformers module makes result texts summarizations
  sum-transformers:
    enabled: false
    # You can set directly an inference URL of this module without deploying it with this release.
    # You can do so by setting a value for the `inferenceUrl` here AND by setting the `enable` to `false`
    inferenceUrl: {}
    tag: facebook-bart-large-cnn-1.0.0
    repo: semitechnologies/sum-transformers
    registry: docker.io
    replicas: 1
    fullnameOverride: sum-transformers
    livenessProbe:
      initialDelaySeconds: 120
      periodSeconds: 3
      timeoutSeconds: 3
    readinessProbe:
      initialDelaySeconds: 120
      periodSeconds: 3
    envconfig:
      # enable for CUDA support. Your K8s cluster needs to be configured
      # accordingly and you need to explicitly set GPU requests & limits below
      enable_cuda: false

      # only used when cuda is enabled
      nvidia_visible_devices: all
      nvidia_driver_capabilities: compute,utility

      # only used when cuda is enabled
      ld_library_path: /usr/local/nvidia/lib64

    resources:
      requests:
        cpu: '1000m'
        memory: '3000Mi'

        # enable if running with CUDA support
        # nvidia.com/gpu: 1
      limits:
        cpu: '1000m'
        memory: '5000Mi'

        # enable if running with CUDA support
        # nvidia.com/gpu: 1
    
    # It is possible to add a ServiceAccount to this module's Pods, it can be
    # used in cases where the module is in a private registry and you want to
    # give access to the registry only to this pod.
    # NOTE: if not set the root `serviceAccountName` config will be used.
    serviceAccountName:

    # You can guide where the pods are scheduled on a per-module basis,
    # as well as for Weaviate overall. Each module accepts nodeSelector,
    # tolerations, and affinity configuration. If it is set on a per-
    # module basis, this configuration overrides the global config.

    nodeSelector:
    tolerations:
    affinity:

  # by choosing the default vectorizer module, you can tell Weaviate to always
  # use this module as the vectorizer if nothing else is specified. Can be
  # overwritten on a per-class basis.
  # set to text2vec-transformers if running with transformers instead
  default_vectorizer_module: none

# It is also possible to configure authentication and authorization through a
# custom configmap The authorization and authentication values defined in
# values.yaml will be ignored when defining a custom config map.
custom_config_map:
  enabled: false
  name: 'custom-config'

# Pass any annotations to Weaviate pods
annotations:

nodeSelector:

tolerations:

affinity:
  podAntiAffinity:
    preferredDuringSchedulingIgnoredDuringExecution:
      - weight: 1
        podAffinityTerm:
          topologyKey: "kubernetes.io/hostname"
          labelSelector:
            matchExpressions:
              - key: "app"
                operator: In
                values:
                  - weaviate