forked from phoenix-oss/llama-stack-mirror
252a487085
# What does this PR do? Adds nvidia as a safety provider by interfacing with the nemo guardrails microservice. This enables checking user’s input or the LLM’s output against input and output guardrails by using the `/v1/guardrails/checks` endpoint of the[ guardrails API.](https://developer.nvidia.com/docs/nemo-microservices/guardrails/source/guides/checks-guide.html) ## Test Plan Deploy nemo guardrails service following the documentation: https://developer.nvidia.com/docs/nemo-microservices/guardrails/source/getting-started/deploy-docker.html ### Standalone: ```bash (venv) local-cdgamarose@a1u1g-rome-0153:~/llama-stack$ pytest -v -s llama_stack/providers/tests/safety/test_safety.py --providers inference=nvidia,safety=nvidia --safety-shield meta/llama-3.1-8b-instruct =================================================================================== test session starts =================================================================================== platform linux -- Python 3.10.12, pytest-8.3.4, pluggy-1.5.0 -- /localhome/local-cdgamarose/llama-stack/venv/bin/python3 cachedir: .pytest_cache metadata: {'Python': '3.10.12', 'Platform': 'Linux-5.15.0-122-generic-x86_64-with-glibc2.35', 'Packages': {'pytest': '8.3.4', 'pluggy': '1.5.0'}, 'Plugins': {'metadata': '3.1.1', 'asyncio': '0.25.3', 'anyio': '4.8.0', 'html': '4.1.1'}} rootdir: /localhome/local-cdgamarose/llama-stack configfile: pyproject.toml plugins: metadata-3.1.1, asyncio-0.25.3, anyio-4.8.0, html-4.1.1 asyncio: mode=strict, asyncio_default_fixture_loop_scope=None collected 2 items llama_stack/providers/tests/safety/test_safety.py::TestSafety::test_shield_list[--inference=nvidia:safety=nvidia] Initializing NVIDIASafetyAdapter(http://0.0.0.0:7331)... PASSED llama_stack/providers/tests/safety/test_safety.py::TestSafety::test_run_shield[--inference=nvidia:safety=nvidia] PASSED ============================================================================== 2 passed, 2 warnings in 4.78s ============================================================================== ``` ### Distribution: ``` llama stack run llama_stack/templates/nvidia/run-with-safety.yaml curl -v -X 'POST' "http://localhost:8321/v1/safety/run-shield" -H 'accept: application/json' -H 'Content-Type: application/json' -d '{"shield_id": "meta/llama-3.1-8b-instruct", "messages":[{"role": "user", "content": "you are stupid"}]}' {"violation":{"violation_level":"error","user_message":"Sorry I cannot do this.","metadata":{"self check input":{"status":"blocked"}}}} ``` [//]: # (## Documentation) --------- Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
80 lines
2.7 KiB
Markdown
80 lines
2.7 KiB
Markdown
<!-- This file was auto-generated by distro_codegen.py, please edit source -->
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# NVIDIA Distribution
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The `llamastack/distribution-nvidia` distribution consists of the following provider configurations.
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| API | Provider(s) |
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|-----|-------------|
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| agents | `inline::meta-reference` |
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| datasetio | `inline::localfs` |
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| eval | `inline::meta-reference` |
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| inference | `remote::nvidia` |
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| safety | `remote::nvidia` |
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| scoring | `inline::basic` |
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| telemetry | `inline::meta-reference` |
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| tool_runtime | `inline::rag-runtime` |
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| vector_io | `inline::faiss` |
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### Environment Variables
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The following environment variables can be configured:
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- `NVIDIA_API_KEY`: NVIDIA API Key (default: ``)
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- `GUARDRAILS_SERVICE_URL`: URL for the NeMo Guardrails Service (default: `http://0.0.0.0:7331`)
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- `INFERENCE_MODEL`: Inference model (default: `Llama3.1-8B-Instruct`)
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- `SAFETY_MODEL`: Name of the model to use for safety (default: `meta/llama-3.1-8b-instruct`)
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### Models
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The following models are available by default:
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- `meta/llama3-8b-instruct (aliases: meta-llama/Llama-3-8B-Instruct)`
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- `meta/llama3-70b-instruct (aliases: meta-llama/Llama-3-70B-Instruct)`
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- `meta/llama-3.1-8b-instruct (aliases: meta-llama/Llama-3.1-8B-Instruct)`
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- `meta/llama-3.1-70b-instruct (aliases: meta-llama/Llama-3.1-70B-Instruct)`
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- `meta/llama-3.1-405b-instruct (aliases: meta-llama/Llama-3.1-405B-Instruct-FP8)`
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- `meta/llama-3.2-1b-instruct (aliases: meta-llama/Llama-3.2-1B-Instruct)`
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- `meta/llama-3.2-3b-instruct (aliases: meta-llama/Llama-3.2-3B-Instruct)`
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- `meta/llama-3.2-11b-vision-instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)`
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- `meta/llama-3.2-90b-vision-instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)`
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- `nvidia/llama-3.2-nv-embedqa-1b-v2 `
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- `nvidia/nv-embedqa-e5-v5 `
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- `nvidia/nv-embedqa-mistral-7b-v2 `
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- `snowflake/arctic-embed-l `
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### Prerequisite: API Keys
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Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](https://build.nvidia.com/).
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## Running Llama Stack with NVIDIA
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You can do this via Conda (build code) or Docker which has a pre-built image.
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### Via Docker
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This method allows you to get started quickly without having to build the distribution code.
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```bash
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LLAMA_STACK_PORT=5001
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docker run \
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-it \
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-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
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-v ./run.yaml:/root/my-run.yaml \
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llamastack/distribution-nvidia \
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--yaml-config /root/my-run.yaml \
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--port $LLAMA_STACK_PORT \
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--env NVIDIA_API_KEY=$NVIDIA_API_KEY
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```
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### Via Conda
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```bash
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llama stack build --template nvidia --image-type conda
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llama stack run ./run.yaml \
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--port 5001 \
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--env NVIDIA_API_KEY=$NVIDIA_API_KEY
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--env INFERENCE_MODEL=$INFERENCE_MODEL
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```
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