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https://github.com/meta-llama/llama-stack.git
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5844c2da68
# What does this PR do? This is not part of the official OpenAI API, but we'll use this for the logs UI. In order to support more filtering options, I'm adopting the newly introduced sql store in in place of the kv store. ## Test Plan Added integration/unit tests.
121 lines
3.4 KiB
YAML
121 lines
3.4 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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- inference
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- post_training
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- safety
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- scoring
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- telemetry
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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: 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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namespace: null
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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: 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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namespace: null
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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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telemetry:
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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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service_name: ${env.OTEL_SERVICE_NAME:}
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sinks: ${env.TELEMETRY_SINKS:console,sqlite}
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sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/trace_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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namespace: null
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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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config: {}
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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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config: {}
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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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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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