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# 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>
40 lines
1.5 KiB
Python
40 lines
1.5 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import os
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from typing import Any
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from pydantic import BaseModel, Field
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from llama_stack.schema_utils import json_schema_type
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@json_schema_type
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class NVIDIASafetyConfig(BaseModel):
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"""
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Configuration for the NVIDIA Guardrail microservice endpoint.
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Attributes:
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guardrails_service_url (str): A base url for accessing the NVIDIA guardrail endpoint, e.g. http://0.0.0.0:7331
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config_id (str): The ID of the guardrails configuration to use from the configuration store
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(https://developer.nvidia.com/docs/nemo-microservices/guardrails/source/guides/configuration-store-guide.html)
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"""
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guardrails_service_url: str = Field(
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default_factory=lambda: os.getenv("GUARDRAILS_SERVICE_URL", "http://0.0.0.0:7331"),
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description="The url for accessing the Guardrails service",
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)
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config_id: str | None = Field(
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default_factory=lambda: os.getenv("NVIDIA_GUARDRAILS_CONFIG_ID", "self-check"),
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description="Guardrails configuration ID to use from the Guardrails configuration store",
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)
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@classmethod
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def sample_run_config(cls, **kwargs) -> dict[str, Any]:
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return {
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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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}
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