feat: Add Nvidia e2e beginner notebook and tool calling notebook (#1964)

# 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>

llama_stack/providers/remote/datasetio/nvidia/README.md

@@ -32,7 +32,6 @@ import os
 
 os.environ["NVIDIA_API_KEY"] = "your-api-key"
 os.environ["NVIDIA_CUSTOMIZER_URL"] = "http://nemo.test"
-os.environ["NVIDIA_USER_ID"] = "llama-stack-user"
 os.environ["NVIDIA_DATASET_NAMESPACE"] = "default"
 os.environ["NVIDIA_PROJECT_ID"] = "test-project"
 from llama_stack.distribution.library_client import LlamaStackAsLibraryClient

llama_stack/providers/remote/datasetio/nvidia/datasetio.py

@@ -36,6 +36,10 @@ class NvidiaDatasetIOAdapter:
         url = f"{self.config.datasets_url}{path}"
         request_headers = self.headers.copy()
 
+        # Set default Content-Type for JSON requests
+        if json is not None:
+            request_headers["Content-Type"] = "application/json"
+
         if headers:
             request_headers.update(headers)
 

llama_stack/providers/remote/safety/nvidia/config.py

@@ -25,13 +25,16 @@ class NVIDIASafetyConfig(BaseModel):
 
     guardrails_service_url: str = Field(
         default_factory=lambda: os.getenv("GUARDRAILS_SERVICE_URL", "http://0.0.0.0:7331"),
-        description="The url for accessing the guardrails service",
+        description="The url for accessing the Guardrails service",
+    )
+    config_id: str | None = Field(
+        default_factory=lambda: os.getenv("NVIDIA_GUARDRAILS_CONFIG_ID", "self-check"),
+        description="Guardrails configuration ID to use from the Guardrails configuration store",
     )
-    config_id: str | None = Field(default="self-check", description="Config ID to use from the config store")
 
     @classmethod
     def sample_run_config(cls, **kwargs) -> dict[str, Any]:
         return {
             "guardrails_service_url": "${env.GUARDRAILS_SERVICE_URL:http://localhost:7331}",
-            "config_id": "self-check",
+            "config_id": "${env.NVIDIA_GUARDRAILS_CONFIG_ID:self-check}",
         }

llama_stack/templates/nvidia/doc_template.md

@@ -144,3 +144,6 @@ llama stack run ./run.yaml \
   --env NVIDIA_API_KEY=$NVIDIA_API_KEY \
   --env INFERENCE_MODEL=$INFERENCE_MODEL
 ```
+
+## Example Notebooks
+For examples of how to use the NVIDIA Distribution to run inference, fine-tune, evaluate, and run safety checks on your LLMs, you can reference the example notebooks in `docs/notebooks/nvidia`.

llama_stack/templates/nvidia/nvidia.py

@@ -130,6 +130,10 @@ def get_distribution_template() -> DistributionTemplate:
                 "http://0.0.0.0:7331",
                 "URL for the NeMo Guardrails Service",
             ),
+            "NVIDIA_GUARDRAILS_CONFIG_ID": (
+                "self-check",
+                "NVIDIA Guardrail Configuration ID",
+            ),
             "NVIDIA_EVALUATOR_URL": (
                 "http://0.0.0.0:7331",
                 "URL for the NeMo Evaluator Service",

llama_stack/templates/nvidia/run-with-safety.yaml

@@ -23,7 +23,7 @@ providers:
     provider_type: remote::nvidia
     config:
       guardrails_service_url: ${env.GUARDRAILS_SERVICE_URL:http://localhost:7331}
-      config_id: self-check
+      config_id: ${env.NVIDIA_GUARDRAILS_CONFIG_ID:self-check}
   vector_io:
   - provider_id: faiss
     provider_type: inline::faiss
@@ -37,7 +37,7 @@ providers:
     provider_type: remote::nvidia
     config:
       guardrails_service_url: ${env.GUARDRAILS_SERVICE_URL:http://localhost:7331}
-      config_id: self-check
+      config_id: ${env.NVIDIA_GUARDRAILS_CONFIG_ID:self-check}
   agents:
   - provider_id: meta-reference
     provider_type: inline::meta-reference

llama_stack/templates/nvidia/run.yaml

@@ -32,7 +32,7 @@ providers:
     provider_type: remote::nvidia
     config:
       guardrails_service_url: ${env.GUARDRAILS_SERVICE_URL:http://localhost:7331}
-      config_id: self-check
+      config_id: ${env.NVIDIA_GUARDRAILS_CONFIG_ID:self-check}
   agents:
   - provider_id: meta-reference
     provider_type: inline::meta-reference