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https://github.com/meta-llama/llama-stack.git
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40e2c97915
# What does this PR do? This PR contains two sets of notebooks that serve as reference material for developers getting started with Llama Stack using the NVIDIA Provider. Developers should be able to execute these notebooks end-to-end, pointing to their NeMo Microservices deployment. 1. `beginner_e2e/`: Notebook that walks through a beginner end-to-end workflow that covers creating datasets, running inference, customizing and evaluating models, and running safety checks. 2. `tool_calling/`: Notebook that is ported over from the [Data Flywheel & Tool Calling notebook](https://github.com/NVIDIA/GenerativeAIExamples/tree/main/nemo/data-flywheel) that is referenced in the NeMo Microservices docs. I updated the notebook to use the Llama Stack client wherever possible, and added relevant instructions. [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan - Both notebook folders contain READMEs with pre-requisites. To manually test these notebooks, you'll need to have a deployment of the NeMo Microservices Platform and update the `config.py` file with your deployment's information. - I've run through these notebooks manually end-to-end to verify each step works. [//]: # (## Documentation) --------- Co-authored-by: Jash Gulabrai <jgulabrai@nvidia.com>
121 lines
3.5 KiB
YAML
121 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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- 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: ${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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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: ${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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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:\u200B}"
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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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