llama-stack/llama_stack/templates/nvidia/run-with-safety.yaml
Rashmi Pawar e6bbf8d20b
feat: Add NVIDIA NeMo datastore (#1852)
# What does this PR do?
Implemetation of NeMO Datastore register, unregister API.

Open Issues: 
- provider_id gets set to `localfs` in client.datasets.register() as it
is specified in routing_tables.py: DatasetsRoutingTable
see: #1860

Currently I have passed `"provider_id":"nvidia"` in metadata and have
parsed that in `DatasetsRoutingTable`
(Not the best approach, but just a quick workaround to make it work for
now.)

## Test Plan
- Unit test cases: `pytest
tests/unit/providers/nvidia/test_datastore.py`
```bash
========================================================== test session starts ===========================================================
platform linux -- Python 3.10.0, pytest-8.3.5, pluggy-1.5.0
rootdir: /home/ubuntu/llama-stack
configfile: pyproject.toml
plugins: anyio-4.9.0, asyncio-0.26.0, nbval-0.11.0, metadata-3.1.1, html-4.1.1, cov-6.1.0
asyncio: mode=strict, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function
collected 2 items                                                                                                                        

tests/unit/providers/nvidia/test_datastore.py ..                                                                                   [100%]

============================================================ warnings summary ============================================================

====================================================== 2 passed, 1 warning in 0.84s ======================================================
```

cc: @dglogo, @mattf, @yanxi0830
2025-04-28 09:41:59 -07:00

115 lines
3.2 KiB
YAML

version: '2'
image_name: nvidia
apis:
- agents
- datasetio
- eval
- inference
- post_training
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
providers:
inference:
- provider_id: nvidia
provider_type: remote::nvidia
config:
url: ${env.NVIDIA_BASE_URL:https://integrate.api.nvidia.com}
api_key: ${env.NVIDIA_API_KEY:}
append_api_version: ${env.NVIDIA_APPEND_API_VERSION:True}
- provider_id: nvidia
provider_type: remote::nvidia
config:
guardrails_service_url: ${env.GUARDRAILS_SERVICE_URL:http://localhost:7331}
config_id: self-check
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/faiss_store.db
safety:
- provider_id: nvidia
provider_type: remote::nvidia
config:
guardrails_service_url: ${env.GUARDRAILS_SERVICE_URL:http://localhost:7331}
config_id: self-check
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/agents_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:\u200B}"
sinks: ${env.TELEMETRY_SINKS:console,sqlite}
sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/nvidia/trace_store.db}
eval:
- provider_id: nvidia
provider_type: remote::nvidia
config:
evaluator_url: ${env.NVIDIA_EVALUATOR_URL:http://localhost:7331}
post_training:
- provider_id: nvidia
provider_type: remote::nvidia
config:
api_key: ${env.NVIDIA_API_KEY:}
dataset_namespace: ${env.NVIDIA_DATASET_NAMESPACE:default}
project_id: ${env.NVIDIA_PROJECT_ID:test-project}
customizer_url: ${env.NVIDIA_CUSTOMIZER_URL:http://nemo.test}
datasetio:
- provider_id: localfs
provider_type: inline::localfs
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/localfs_datasetio.db
- provider_id: nvidia
provider_type: remote::nvidia
config:
api_key: ${env.NVIDIA_API_KEY:}
dataset_namespace: ${env.NVIDIA_DATASET_NAMESPACE:default}
project_id: ${env.NVIDIA_PROJECT_ID:test-project}
datasets_url: ${env.NVIDIA_DATASETS_URL:http://nemo.test}
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
tool_runtime:
- provider_id: rag-runtime
provider_type: inline::rag-runtime
config: {}
metadata_store:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/registry.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: nvidia
model_type: llm
- metadata: {}
model_id: ${env.SAFETY_MODEL}
provider_id: nvidia
model_type: llm
shields:
- shield_id: ${env.SAFETY_MODEL}
provider_id: nvidia
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups:
- toolgroup_id: builtin::rag
provider_id: rag-runtime
server:
port: 8321