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values-default.yaml
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image:
# registry where weaviate image is stored
registry: cr.weaviate.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: 1.26.3
repo: semitechnologies/weaviate
# Image pull policy: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policy
pullPolicy: IfNotPresent
pullSecrets: []
# 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"
# it is possible to change the sysctl's 'vm.max_map_count' using initContainer for Weaviate,
# the init Container runs before Weaviate Container and sets the value for the WHOLE node
# to the one provided below.
# it is possible to run additional initContainer before Weaviate is up and running. You can specify the
# containers as a list in `extraInitContainers`, exactly how they are defined in a kubernetes manifest:
# https://kubernetes.io/docs/concepts/workloads/pods/init-containers/
initContainers:
sysctlInitContainer:
enabled: true
sysctlVmMaxMapCount: 524288
image:
registry: docker.io
repo: alpine
tag: latest
pullPolicy: IfNotPresent
ensureFileOwnershipContainer:
# This init container sets the file ownerships of /var/lib/weaviate directory to the ones set in
# containerSecurityContext.runAsUser and containerSecurityContext.fsGroup settings to ensure that Weaviate is able
# to start in unprivileged configuration.
# Enable this init container only if Weaviate was configured previously without security context
# and now containerSecurityContext is provided to run Weaviate container with non-root user.
# Please be sure to set at least containerSecurityContext.runAsUser and containerSecurityContext.fsGroup.
enabled: false
extraInitContainers: {}
# - image: some-image
# name: some-name
# 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
# Define how pods will be created. Possible values: OrderedReady | Parallel
# OrderedReady - pods will be created one after another
# Parallel - all pods will be created at once
podManagementPolicy: Parallel
updateStrategy:
type: RollingUpdate
# This setting is only available in K8s v1.24 and higher.
# Setting maxUnavailable to 100% results in removing all of the pods
# and re-creating them in parallel all at once.
# rollingUpdate:
# maxUnavailable: 100%
resources: {}
# requests:
# cpu: '500m'
# memory: '300Mi'
# limits:
# cpu: '1000m'
# memory: '1Gi'
# security Context for the Weaviate Pods. The configurations are the same as setting them
# as described here: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/
securityContext: {}
# Security context for the Weaviate container. Override overlapping settings made at the Pod level.
containerSecurityContext: {}
# runAsUser: 1000
# runAsGroup: 1000
# fsGroup: 1000
# fsGroupChangePolicy: "OnRootMismatch"
# runAsNonRoot: true
# allowPrivilegeEscalation: false
# privileged: false
# readOnlyRootFilesystem: true
# Add a service account to 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:
# Kubernetes Cluster domain name, used for resolving intra-cluster requests, i.e
# between instances of weaviate.
# Note: The final '.' on the end of the hostname makes it a FQDN, and is required for
# DNS to resolve in all kubernetes environments.
# See https://github.com/weaviate/weaviate-helm/issues/175 for details.
clusterDomain: cluster.local.
# 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: {}
# The service controls how weaviate gRPC endpoint is exposed to the outside world.
# If you don't want a public load balancer, you can also choose 'ClusterIP' or `NodePort`
# to make weaviate gRPC port be only accessible within your cluster.
# This service is by default enabled but if you don't want it to be deployed in your
# environment then it can be disabled by setting enabled: false option.
grpcService:
enabled: true
name: weaviate-grpc
ports:
- name: grpc
protocol: TCP
port: 50051
# 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: {}
# The service monitor defines prometheus monitoring for a set of services
# https://github.com/prometheus-operator/prometheus-operator/blob/main/Documentation/api.md#monitoring.coreos.com/v1.ServiceMonitor
# Make sure to set the following prometheus values if deploying observability with the kube-prometheus-stack helm chart:
# - prometheus.prometheusSpec.serviceMonitorSelectorNilUsesHelmValues: false
serviceMonitor:
enabled: false
interval: 30s
scrapeTimeout: 10s
# Adjust liveness, readiness and startup probes configuration
# below is an example that can be used to switch the probeType to exec command
# readinessProbe: # (Compatible with liveness, readiness and startup probe configurations)
# probeType: exec
# probe:
# exec:
# command: ["/bin/sh", "-c", "wget --spider --server-response --tries=1 --timeout=30 -o /dev/null localhost:8080/v1/.well-known/ready"]
startupProbe:
# For kubernetes versions prior to 1.18 startupProbe is not supported thus can be disabled.
enabled: false
probeType: httpGet
probe:
httpGet:
path: /v1/.well-known/ready
port: 8080
initialDelaySeconds: 300
periodSeconds: 60
failureThreshold: 50
successThreshold: 1
timeoutSeconds: 3
livenessProbe:
probeType: httpGet
probe:
httpGet:
path: /v1/.well-known/live
port: 8080
initialDelaySeconds: 900
periodSeconds: 10
failureThreshold: 30
successThreshold: 1
timeoutSeconds: 3
readinessProbe:
probeType: httpGet
probe:
httpGet:
path: /v1/.well-known/ready
port: 8080
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
# This configuration allows to add API keys to Weaviate. This configuration allows only
# plain text API Keys, if you want to store the API Keys in a Kubernetes secret you can
# configure the same configuration with ENV Vars. Read the `env` section below on what
# needs to be configured. If using ENV Vars over this make sure to comment out the whole
# `apikey` section (as it is by default). ENV Vars has priority over this config.
# apikey:
# enabled: false
# # Any number of allowed API Keys as plain text
# allowed_keys:
# - readOnly-plainText-API-Key
# - admin-plainText-API-Key
# # You can either set a single user for all the listed Allowed API keys OR
# # one user per API Key, i.e. length(apikey.allowed_keys) == length(apikey.users) OR
# # length(apikey.users) == 1
# # NOTE: Make sure the lister Users are added to the Authorization as well.
# users:
# - api-key-user-readOnly
# - api-key-user-admin
oidc:
enabled: false
# issuer: ''
# username_claim: ''
# groups_claim: ''
# client_id: ''
authorization:
admin_list:
enabled: false
# users:
# - admin_user1
# - admin_user2
# - api-key-user-admin
# read_only_users:
# - readonly_user1
# - readonly_user2
# - api-key-user-readOnly
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
# Set RAFT cluster expected number of voter nodes at bootstrap.
# By default helm automatically sets this value based on the cluster size.
# RAFT_BOOTSTRAP_EXPECT: 1
# Set RAFT cluster bootstrap timeout (in seconds), default is 90 (seconds)
# RAFT_BOOTSTRAP_TIMEOUT: 90
# Set manually RAFT voter nodes.
# RAFT_JOIN value is automatically generated by "raft_configuration"
# template, but if someone wants to set this value manually then it can be done
# by setting RAFT_JOIN environment variable, example: RAFT_JOIN: "weaviate-0,weaviate-1"
# Please notice that in this case RAFT_BOOTSTRAP_EXPECT setting needs to be also adjusted manually
# to match the number of RAFT voters, so if there are 2 nodes set using RAFT_JOIN variable
# then RAFT_BOOTSTRAP_EXPECT needs to be equal 2 also.
# RAFT_JOIN: "weaviate-0"
# Set to true if voters nodes should handle only schema. With this setting enabled
# voter nodes will not accept any data, one needs to resize the cluster using replicas
# setting so that replicas > voters.
# RAFT_METADATA_ONLY_VOTERS: false
# 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
PROMETHEUS_MONITORING_GROUP: 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
##########################
# API Keys with ENV Vars #
##########################
# If using ENV Vars to set up API Keys make sure to have `authentication.apikey` block commented out
# to avoid any future changes. ENV Vars has priority over the config above `authentication.apikey`.
# If using `authentication.apikey `the below ENV Vars will be used because they have priority,
# so comment them out to avoid any future changes.
# Enables API key authentication. If it is set to 'false' the AUTHENTICATION_APIKEY_ALLOWED_KEYS
# and AUTHENTICATION_APIKEY_USERS will not have any effect.
# AUTHENTICATION_APIKEY_ENABLED: 'true'
# List one or more keys, separated by commas. Each key corresponds to a specific user identity below.
# If you want to use a kubernetes secret for the API Keys comment out this Variable and use the one in `envSecrets` below
# AUTHENTICATION_APIKEY_ALLOWED_KEYS: 'jane-secret-key,ian-secret-key' (plain text)
# List one or more user identities, separated by commas. You can have only one User for all the keys or one user per key.
# The User/s can be a simple name or an email, no matter if it exists or not.
# NOTE: Make sure to add the users to the authorization above overwise they will not be allowed to interact with Weaviate.
# AUTHENTICATION_APIKEY_USERS: '[email protected],ian-smith'
envSecrets:
# create a Kubernetes secret with AUTHENTICATION_APIKEY_ALLOWED_KEYS key and its respective value
# AUTHENTICATION_APIKEY_ALLOWED_KEYS: name-of-the-k8s-secret-containing-the-comma-separated-api-keys
# Configure offload providers
offload:
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 data should be saved, this setting is mandatory
OFFLOAD_S3_BUCKET: weaviate-offload
# 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.
# OFFLOAD_S3_ENDPOINT: custom.minio.endpoint.address
# Optional setting. Defaults to true.
# Set this option if you don't want to use SSL.
# OFFLOAD_S3_USE_SSL: true
# Optional setting. Defaults to false.
# Set this option if you wan't Weaviate to create
# the bucket used for offloading tenants. Otherwise,
# if set to false Weaviate expects the bucket to be
# already created with the OFFLOAD_S3_BUCKET name
# OFFLOAD_S3_BUCKET_AUTO_CREATE: 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
# 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 (plain text)
# 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:
# Enable deployment of this module
enabled: false
# The configuration below is ignored if enabled==false
fullnameOverride: contextionary
tag: en0.16.0-v1.2.1
repo: semitechnologies/contextionary
registry: cr.weaviate.io
replicas: 1
strategy:
type: RollingUpdate
imagePullPolicy: IfNotPresent
imagePullSecrets: []
priorityClassName: ""
livenessProbe:
initialDelaySeconds: 120
periodSeconds: 3
timeoutSeconds: 3
readinessProbe:
initialDelaySeconds: 120
periodSeconds: 3
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'
# security Context for the Contextionary Pods. The configurations are the same as setting them
# as described here: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/
securityContext: {}
# 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 deployment of this 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: sentence-transformers-paraphrase-multilingual-MiniLM-L12-v2
repo: semitechnologies/transformers-inference
registry: cr.weaviate.io
replicas: 1
strategy:
type: RollingUpdate
imagePullPolicy: IfNotPresent
imagePullSecrets: []
priorityClassName: ""
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
# security Context for the Transformers Pods. The configurations are the same as setting them
# as described here: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/
# applies to passageQueryService below
securityContext: {}
# 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: {}
# The configuration below is ignored if enabled==false
tag: facebook-dpr-ctx_encoder-single-nq-base
repo: semitechnologies/transformers-inference
registry: cr.weaviate.io
imagePullPolicy: IfNotPresent
imagePullSecrets: []
priorityClassName: ""
replicas: 1
strategy:
type: RollingUpdate
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
# security Context for the Transformers Pods. The configurations are the same as setting them
# as described here: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/
# applies to passageQueryService below
securityContext: {}
# 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:
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: {}
# The configuration below is ignored if enabled==false
tag: facebook-dpr-question_encoder-single-nq-base
repo: semitechnologies/transformers-inference
registry: cr.weaviate.io
imagePullPolicy: IfNotPresent
imagePullSecrets: []
priorityClassName: ""
replicas: 1
strategy:
type: RollingUpdate
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
# security Context for the Transformers Pods. The configurations are the same as setting them
# as described here: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/
# applies to passageQueryService below
securityContext: {}
# 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-gpt4all is a vectorizer module that allows to run ML models from
# nomic-ai/gpt4all: https://docs.gpt4all.io/gpt4all_python_embedding.html
# These models run only on CPU only and on x86_64 arch
# The ML model is containerized in a Weaviate compatible way.
# If you want to run a different model that published ones you can follow the
# tutorial from here on how to create such a container: https://github.com/weaviate/t2v-gpt4all-models
text2vec-gpt4all:
# Enable deployment of this 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
tag: all-MiniLM-L6-v2
repo: semitechnologies/gpt4all-inference
registry: cr.weaviate.io
replicas: 1
strategy:
type: RollingUpdate
imagePullPolicy: IfNotPresent
imagePullSecrets: []
priorityClassName: ""
fullnameOverride: gpt4all-inference
livenessProbe:
initialDelaySeconds: 120
periodSeconds: 3
timeoutSeconds: 3
readinessProbe:
initialDelaySeconds: 120
periodSeconds: 3
resources: {}
# requests:
# cpu: '1000m'
# memory: '3000Mi'
# limits:
# cpu: '1000m'
# memory: '5000Mi'
# security Context for the Contextionary Pods. The configurations are the same as setting them
# as described here: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/
securityContext: {}
# 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-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. You can set either the OpenAI's Service API key
# OR the Azure OpenAI's Service Key. Note that you cannot set both of the at the same time.
apiKey: ''
azureApiKey: ''
# 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 text2vec-palm module uses Google PaLM Embeddings API
# to dynamically compute vector embeddings based on the
# sentence's context.
# More information about Google PaLM Embeddings API can be found here:
# https://developers.generativeai.google/
text2vec-palm:
# enable if you want to use Google PaLM module
enabled: false
# Set your Google PaLM API Key to be passed to Weaviate pod as
# an environment variable
apiKey: ''
# The text2vec-jinaai module uses JinaAI Embeddings API
# to dynamically compute vector embeddings based on the
# sentence's context.
# More information about JinaAI Embeddings API can be found here:
# https://jina.ai/embeddings
text2vec-jinaai:
# enable if you want to use JinaAI module
enabled: false
# Set your JinaAI 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 text2vec-aws module uses AWS Bedrock service
# to dynamically compute vector embeddings based on the
# sentence's context.
# More information about AWS Bedrock service can be found here: https://docs.aws.amazon.com/bedrock/
text2vec-aws:
# enable if you want to use AWS module
enabled: false
# For Weaviate to be able to use AWS Bedrock service it needs a user credentials to authenticate to AWS.
# The User must have permissions to use AWS Bedrock service AI.
# 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
# The text2vec-voyageai module uses VoaygeAI Embeddings API
# to dynamically compute vector embeddings based on the
# sentence's context.
# More information about VoaygeAI Embeddings API can be found here:
# https://www.voyageai.com/
text2vec-voyageai:
# enable if you want to use VoaygeAI module
enabled: false
# Set your VoaygeAI API Key to be passed to Weaviate pod as
# an environment variable
apiKey: ''
# The text2vec-ollama module uses Ollama Embeddings API
# to dynamically compute vector embeddings based on the
# sentence's context.
# More information about Ollama Embeddings API can be found here:
# https://github.com/ollama/ollama/blob/main/docs/api.md#generate-embeddings
# Please note that you have to deploy Ollama container by yourself
# and then properly point the Ollama endpoint using module settings.
# More about this configuration can be found here:
# https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/text2vec-ollama#ollama-endpoint
text2vec-ollama:
# enable if you want to use Ollama module
enabled: false
# The text2vec-octoai module uses OctoAI Embeddings API
# to dynamically compute vector embeddings based on the
# sentence's context.
# More information about OctoAI Embeddings API can be found here: