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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
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