forked from phoenix-oss/llama-stack-mirror
34ab7a3b6c
Lint check in main branch is failing. This fixes the lint check after we moved to ruff in https://github.com/meta-llama/llama-stack/pull/921. We need to move to a `ruff.toml` file as well as fixing and ignoring some additional checks. Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
126 lines
4.1 KiB
Python
126 lines
4.1 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 tempfile
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import pytest
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import pytest_asyncio
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from llama_stack.apis.models import ModelInput, ModelType
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from llama_stack.distribution.datatypes import Api, Provider
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from llama_stack.providers.inline.agents.meta_reference import (
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MetaReferenceAgentsImplConfig,
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)
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from llama_stack.providers.tests.resolver import construct_stack_for_test
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from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
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from ..conftest import ProviderFixture, remote_stack_fixture
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def pick_inference_model(inference_model):
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# This is not entirely satisfactory. The fixture `inference_model` can correspond to
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# multiple models when you need to run a safety model in addition to normal agent
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# inference model. We filter off the safety model by looking for "Llama-Guard"
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if isinstance(inference_model, list):
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inference_model = next(m for m in inference_model if "Llama-Guard" not in m)
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assert inference_model is not None
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return inference_model
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@pytest.fixture(scope="session")
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def agents_remote() -> ProviderFixture:
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return remote_stack_fixture()
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@pytest.fixture(scope="session")
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def agents_meta_reference() -> ProviderFixture:
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sqlite_file = tempfile.NamedTemporaryFile(delete=False, suffix=".db")
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return ProviderFixture(
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providers=[
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Provider(
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provider_id="meta-reference",
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provider_type="inline::meta-reference",
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config=MetaReferenceAgentsImplConfig(
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# TODO: make this an in-memory store
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persistence_store=SqliteKVStoreConfig(
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db_path=sqlite_file.name,
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),
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).model_dump(),
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)
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],
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)
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AGENTS_FIXTURES = ["meta_reference", "remote"]
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@pytest_asyncio.fixture(scope="session")
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async def agents_stack(
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request,
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inference_model,
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safety_shield,
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tool_group_input_memory,
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tool_group_input_tavily_search,
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):
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fixture_dict = request.param
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providers = {}
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provider_data = {}
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for key in ["inference", "safety", "vector_io", "agents", "tool_runtime"]:
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fixture = request.getfixturevalue(f"{key}_{fixture_dict[key]}")
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providers[key] = fixture.providers
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if key == "inference":
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providers[key].append(
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Provider(
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provider_id="agents_memory_provider",
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provider_type="inline::sentence-transformers",
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config={},
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)
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)
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if fixture.provider_data:
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provider_data.update(fixture.provider_data)
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inference_models = inference_model if isinstance(inference_model, list) else [inference_model]
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# NOTE: meta-reference provider needs 1 provider per model, lookup provider_id from provider config
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model_to_provider_id = {}
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for provider in providers["inference"]:
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if "model" in provider.config:
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model_to_provider_id[provider.config["model"]] = provider.provider_id
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models = []
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for model in inference_models:
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if model in model_to_provider_id:
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provider_id = model_to_provider_id[model]
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else:
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provider_id = providers["inference"][0].provider_id
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models.append(
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ModelInput(
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model_id=model,
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model_type=ModelType.llm,
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provider_id=provider_id,
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)
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)
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models.append(
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ModelInput(
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model_id="all-MiniLM-L6-v2",
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model_type=ModelType.embedding,
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provider_id="agents_memory_provider",
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metadata={"embedding_dimension": 384},
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)
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)
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test_stack = await construct_stack_for_test(
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[Api.agents, Api.inference, Api.safety, Api.vector_io, Api.tool_runtime],
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providers,
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provider_data,
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models=models,
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shields=[safety_shield] if safety_shield else [],
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tool_groups=[tool_group_input_memory, tool_group_input_tavily_search],
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)
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return test_stack
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