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https://github.com/meta-llama/llama-stack.git
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f22aaef42f
# What does this PR do? remove telemetry as a providable API from the codebase. This includes removing it from generated distributions but also the provider registry, the router, etc since `setup_logger` is tied pretty strictly to `Api.telemetry` being in impls we still need an "instantiated provider" in our implementations. However it should not be auto-routed or provided. So in validate_and_prepare_providers (called from resolve_impls) I made it so that if run_config.telemetry.enabled, we set up the meta-reference "provider" internally to be used so that log_event will work when called. This is the neatest way I think we can remove telemetry from the provider configs but also not need to rip apart the whole "telemetry is a provider" logic just yet, but we can do it internally later without disrupting users. so telemetry is removed from the registry such that if a user puts `telemetry:` as an API in their build/run config it will err out, but can still be used by us internally as we go through this transition. relates to #3806 Signed-off-by: Charlie Doern <cdoern@redhat.com>
120 lines
3.5 KiB
YAML
120 lines
3.5 KiB
YAML
version: 2
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image_name: nvidia
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apis:
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- agents
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- datasetio
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- eval
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- files
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- inference
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- post_training
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- safety
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- scoring
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- tool_runtime
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- vector_io
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providers:
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inference:
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- provider_id: nvidia
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provider_type: remote::nvidia
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config:
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url: ${env.NVIDIA_BASE_URL:=https://integrate.api.nvidia.com}
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api_key: ${env.NVIDIA_API_KEY:=}
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append_api_version: ${env.NVIDIA_APPEND_API_VERSION:=True}
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- provider_id: nvidia
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provider_type: remote::nvidia
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config:
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guardrails_service_url: ${env.GUARDRAILS_SERVICE_URL:=http://localhost:7331}
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config_id: ${env.NVIDIA_GUARDRAILS_CONFIG_ID:=self-check}
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vector_io:
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- provider_id: faiss
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provider_type: inline::faiss
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config:
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kvstore:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/faiss_store.db
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safety:
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- provider_id: nvidia
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provider_type: remote::nvidia
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config:
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guardrails_service_url: ${env.GUARDRAILS_SERVICE_URL:=http://localhost:7331}
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config_id: ${env.NVIDIA_GUARDRAILS_CONFIG_ID:=self-check}
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agents:
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- provider_id: meta-reference
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provider_type: inline::meta-reference
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config:
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persistence_store:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/agents_store.db
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responses_store:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/responses_store.db
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eval:
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- provider_id: nvidia
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provider_type: remote::nvidia
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config:
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evaluator_url: ${env.NVIDIA_EVALUATOR_URL:=http://localhost:7331}
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post_training:
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- provider_id: nvidia
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provider_type: remote::nvidia
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config:
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api_key: ${env.NVIDIA_API_KEY:=}
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dataset_namespace: ${env.NVIDIA_DATASET_NAMESPACE:=default}
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project_id: ${env.NVIDIA_PROJECT_ID:=test-project}
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customizer_url: ${env.NVIDIA_CUSTOMIZER_URL:=http://nemo.test}
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datasetio:
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- provider_id: localfs
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provider_type: inline::localfs
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config:
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kvstore:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/localfs_datasetio.db
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- provider_id: nvidia
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provider_type: remote::nvidia
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config:
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api_key: ${env.NVIDIA_API_KEY:=}
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dataset_namespace: ${env.NVIDIA_DATASET_NAMESPACE:=default}
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project_id: ${env.NVIDIA_PROJECT_ID:=test-project}
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datasets_url: ${env.NVIDIA_DATASETS_URL:=http://nemo.test}
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scoring:
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- provider_id: basic
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provider_type: inline::basic
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tool_runtime:
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- provider_id: rag-runtime
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provider_type: inline::rag-runtime
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files:
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- provider_id: meta-reference-files
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provider_type: inline::localfs
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config:
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storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/nvidia/files}
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metadata_store:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/files_metadata.db
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metadata_store:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/registry.db
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inference_store:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/inference_store.db
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conversations_store:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/conversations.db
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models:
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- metadata: {}
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model_id: ${env.INFERENCE_MODEL}
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provider_id: nvidia
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model_type: llm
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- metadata: {}
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model_id: ${env.SAFETY_MODEL}
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provider_id: nvidia
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model_type: llm
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shields:
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- shield_id: ${env.SAFETY_MODEL}
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provider_id: nvidia
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vector_dbs: []
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datasets: []
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scoring_fns: []
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benchmarks: []
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tool_groups:
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- toolgroup_id: builtin::rag
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provider_id: rag-runtime
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server:
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port: 8321
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