forked from phoenix-oss/llama-stack-mirror
# What does this PR do? - as title, cleaning up `import *`'s - upgrade tests to make them more robust to bad model outputs - remove import *'s in llama_stack/apis/* (skip __init__ modules) <img width="465" alt="image" src="https://github.com/user-attachments/assets/d8339c13-3b40-4ba5-9c53-0d2329726ee2" /> - run `sh run_openapi_generator.sh`, no types gets affected ## Test Plan ### Providers Tests **agents** ``` pytest -v -s llama_stack/providers/tests/agents/test_agents.py -m "together" --safety-shield meta-llama/Llama-Guard-3-8B --inference-model meta-llama/Llama-3.1-405B-Instruct-FP8 ``` **inference** ```bash # meta-reference torchrun $CONDA_PREFIX/bin/pytest -v -s -k "meta_reference" --inference-model="meta-llama/Llama-3.1-8B-Instruct" ./llama_stack/providers/tests/inference/test_text_inference.py torchrun $CONDA_PREFIX/bin/pytest -v -s -k "meta_reference" --inference-model="meta-llama/Llama-3.2-11B-Vision-Instruct" ./llama_stack/providers/tests/inference/test_vision_inference.py # together pytest -v -s -k "together" --inference-model="meta-llama/Llama-3.1-8B-Instruct" ./llama_stack/providers/tests/inference/test_text_inference.py pytest -v -s -k "together" --inference-model="meta-llama/Llama-3.2-11B-Vision-Instruct" ./llama_stack/providers/tests/inference/test_vision_inference.py pytest ./llama_stack/providers/tests/inference/test_prompt_adapter.py ``` **safety** ``` pytest -v -s llama_stack/providers/tests/safety/test_safety.py -m together --safety-shield meta-llama/Llama-Guard-3-8B ``` **memory** ``` pytest -v -s llama_stack/providers/tests/memory/test_memory.py -m "sentence_transformers" --env EMBEDDING_DIMENSION=384 ``` **scoring** ``` pytest -v -s -m llm_as_judge_scoring_together_inference llama_stack/providers/tests/scoring/test_scoring.py --judge-model meta-llama/Llama-3.2-3B-Instruct pytest -v -s -m basic_scoring_together_inference llama_stack/providers/tests/scoring/test_scoring.py pytest -v -s -m braintrust_scoring_together_inference llama_stack/providers/tests/scoring/test_scoring.py ``` **datasetio** ``` pytest -v -s -m localfs llama_stack/providers/tests/datasetio/test_datasetio.py pytest -v -s -m huggingface llama_stack/providers/tests/datasetio/test_datasetio.py ``` **eval** ``` pytest -v -s -m meta_reference_eval_together_inference llama_stack/providers/tests/eval/test_eval.py pytest -v -s -m meta_reference_eval_together_inference_huggingface_datasetio llama_stack/providers/tests/eval/test_eval.py ``` ### Client-SDK Tests ``` LLAMA_STACK_BASE_URL=http://localhost:5000 pytest -v ./tests/client-sdk ``` ### llama-stack-apps ``` PORT=5000 LOCALHOST=localhost python -m examples.agents.hello $LOCALHOST $PORT python -m examples.agents.inflation $LOCALHOST $PORT python -m examples.agents.podcast_transcript $LOCALHOST $PORT python -m examples.agents.rag_as_attachments $LOCALHOST $PORT python -m examples.agents.rag_with_memory_bank $LOCALHOST $PORT python -m examples.safety.llama_guard_demo_mm $LOCALHOST $PORT python -m examples.agents.e2e_loop_with_custom_tools $LOCALHOST $PORT # Vision model python -m examples.interior_design_assistant.app python -m examples.agent_store.app $LOCALHOST $PORT ``` ### CLI ``` which llama llama model prompt-format -m Llama3.2-11B-Vision-Instruct llama model list llama stack list-apis llama stack list-providers inference llama stack build --template ollama --image-type conda ``` ### Distributions Tests **ollama** ``` llama stack build --template ollama --image-type conda ollama run llama3.2:1b-instruct-fp16 llama stack run ./llama_stack/templates/ollama/run.yaml --env INFERENCE_MODEL=meta-llama/Llama-3.2-1B-Instruct ``` **fireworks** ``` llama stack build --template fireworks --image-type conda llama stack run ./llama_stack/templates/fireworks/run.yaml ``` **together** ``` llama stack build --template together --image-type conda llama stack run ./llama_stack/templates/together/run.yaml ``` **tgi** ``` llama stack run ./llama_stack/templates/tgi/run.yaml --env TGI_URL=http://0.0.0.0:5009 --env INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct ``` ## Sources Please link relevant resources if necessary. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Ran pre-commit to handle lint / formatting issues. - [ ] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [ ] Wrote necessary unit or integration tests.
114 lines
4.2 KiB
Python
114 lines
4.2 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 json
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import logging
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from typing import Any, Dict, List
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from llama_stack.apis.inference import Message
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from llama_stack.apis.safety import (
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RunShieldResponse,
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Safety,
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SafetyViolation,
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ViolationLevel,
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)
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from llama_stack.apis.shields import Shield
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from llama_stack.providers.datatypes import ShieldsProtocolPrivate
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from llama_stack.providers.utils.bedrock.client import create_bedrock_client
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from .config import BedrockSafetyConfig
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logger = logging.getLogger(__name__)
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class BedrockSafetyAdapter(Safety, ShieldsProtocolPrivate):
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def __init__(self, config: BedrockSafetyConfig) -> None:
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self.config = config
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self.registered_shields = []
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async def initialize(self) -> None:
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try:
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self.bedrock_runtime_client = create_bedrock_client(self.config)
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self.bedrock_client = create_bedrock_client(self.config, "bedrock")
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except Exception as e:
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raise RuntimeError("Error initializing BedrockSafetyAdapter") from e
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async def shutdown(self) -> None:
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pass
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async def register_shield(self, shield: Shield) -> None:
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response = self.bedrock_client.list_guardrails(
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guardrailIdentifier=shield.provider_resource_id,
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)
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if (
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not response["guardrails"]
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or len(response["guardrails"]) == 0
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or response["guardrails"][0]["version"] != shield.params["guardrailVersion"]
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):
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raise ValueError(
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f"Shield {shield.provider_resource_id} with version {shield.params['guardrailVersion']} not found in Bedrock"
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)
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async def run_shield(
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self, shield_id: str, messages: List[Message], params: Dict[str, Any] = None
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) -> RunShieldResponse:
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shield = await self.shield_store.get_shield(shield_id)
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if not shield:
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raise ValueError(f"Shield {shield_id} not found")
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"""This is the implementation for the bedrock guardrails. The input to the guardrails is to be of this format
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```content = [
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{
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"text": {
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"text": "Is the AB503 Product a better investment than the S&P 500?"
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}
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}
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]```
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However the incoming messages are of this type UserMessage(content=....) coming from
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https://github.com/meta-llama/llama-models/blob/main/models/llama3/api/datatypes.py
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They contain content, role . For now we will extract the content and default the "qualifiers": ["query"]
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"""
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shield_params = shield.params
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logger.debug(f"run_shield::{shield_params}::messages={messages}")
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# - convert the messages into format Bedrock expects
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content_messages = []
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for message in messages:
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content_messages.append({"text": {"text": message.content}})
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logger.debug(
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f"run_shield::final:messages::{json.dumps(content_messages, indent=2)}:"
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)
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response = self.bedrock_runtime_client.apply_guardrail(
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guardrailIdentifier=shield.provider_resource_id,
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guardrailVersion=shield_params["guardrailVersion"],
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source="OUTPUT", # or 'INPUT' depending on your use case
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content=content_messages,
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)
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if response["action"] == "GUARDRAIL_INTERVENED":
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user_message = ""
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metadata = {}
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for output in response["outputs"]:
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# guardrails returns a list - however for this implementation we will leverage the last values
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user_message = output["text"]
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for assessment in response["assessments"]:
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# guardrails returns a list - however for this implementation we will leverage the last values
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metadata = dict(assessment)
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return RunShieldResponse(
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violation=SafetyViolation(
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user_message=user_message,
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violation_level=ViolationLevel.ERROR,
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metadata=metadata,
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)
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)
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return RunShieldResponse()
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