agents to use tools api (#673)

# What does this PR do?

PR #639 introduced the notion of Tools API and ability to invoke tools
through API just as any resource. This PR changes the Agents to start
using the Tools API to invoke tools. Major changes include:
1) Ability to specify tool groups with AgentConfig
2) Agent gets the corresponding tool definitions for the specified tools
and pass along to the model
3) Attachements are now named as Documents and their behavior is mostly
unchanged from user perspective
4) You can specify args that can be injected to a tool call through
Agent config. This is especially useful in case of memory tool, where
you want the tool to operate on a specific memory bank.
5) You can also register tool groups with args, which lets the agent
inject these as well into the tool call.
6) All tests have been migrated to use new tools API and fixtures
including client SDK tests
7) Telemetry just works with tools API because of our trace protocol
decorator


## Test Plan
```
pytest -s -v -k fireworks llama_stack/providers/tests/agents/test_agents.py  \
   --safety-shield=meta-llama/Llama-Guard-3-8B \
   --inference-model=meta-llama/Llama-3.1-8B-Instruct

pytest -s -v -k together  llama_stack/providers/tests/tools/test_tools.py \
   --safety-shield=meta-llama/Llama-Guard-3-8B \
   --inference-model=meta-llama/Llama-3.1-8B-Instruct

LLAMA_STACK_CONFIG="/Users/dineshyv/.llama/distributions/llamastack-together/together-run.yaml" pytest -v tests/client-sdk/agents/test_agents.py
```
run.yaml:
https://gist.github.com/dineshyv/0365845ad325e1c2cab755788ccc5994

Notebook:
https://colab.research.google.com/drive/1ck7hXQxRl6UvT-ijNRZ-gMZxH1G3cN2d?usp=sharing
This commit is contained in:
Dinesh Yeduguru 2025-01-08 19:01:00 -08:00 committed by GitHub
parent 596afc6497
commit a5c57cd381
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116 changed files with 4959 additions and 2778 deletions

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import pytest
from ..conftest import get_provider_fixture_overrides
from ..inference.fixtures import INFERENCE_FIXTURES
from ..memory.fixtures import MEMORY_FIXTURES
from ..safety.fixtures import SAFETY_FIXTURES
from .fixtures import TOOL_RUNTIME_FIXTURES
DEFAULT_PROVIDER_COMBINATIONS = [
pytest.param(
{
"inference": "together",
"safety": "llama_guard",
"memory": "faiss",
"tool_runtime": "memory_and_search",
},
id="together",
marks=pytest.mark.together,
),
]
def pytest_configure(config):
for mark in ["together"]:
config.addinivalue_line(
"markers",
f"{mark}: marks tests as {mark} specific",
)
def pytest_addoption(parser):
parser.addoption(
"--inference-model",
action="store",
default="meta-llama/Llama-3.2-3B-Instruct",
help="Specify the inference model to use for testing",
)
parser.addoption(
"--safety-shield",
action="store",
default="meta-llama/Llama-Guard-3-1B",
help="Specify the safety shield to use for testing",
)
def pytest_generate_tests(metafunc):
if "tools_stack" in metafunc.fixturenames:
available_fixtures = {
"inference": INFERENCE_FIXTURES,
"safety": SAFETY_FIXTURES,
"memory": MEMORY_FIXTURES,
"tool_runtime": TOOL_RUNTIME_FIXTURES,
}
combinations = (
get_provider_fixture_overrides(metafunc.config, available_fixtures)
or DEFAULT_PROVIDER_COMBINATIONS
)
print(combinations)
metafunc.parametrize("tools_stack", combinations, indirect=True)

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import os
import pytest
import pytest_asyncio
from llama_stack.apis.models import ModelInput, ModelType
from llama_stack.apis.tools import ToolGroupInput
from llama_stack.distribution.datatypes import Api, Provider
from llama_stack.providers.tests.resolver import construct_stack_for_test
from ..conftest import ProviderFixture
@pytest.fixture(scope="session")
def tool_runtime_memory_and_search() -> ProviderFixture:
return ProviderFixture(
providers=[
Provider(
provider_id="memory-runtime",
provider_type="inline::memory-runtime",
config={},
),
Provider(
provider_id="tavily-search",
provider_type="remote::tavily-search",
config={
"api_key": os.environ["TAVILY_SEARCH_API_KEY"],
},
),
Provider(
provider_id="wolfram-alpha",
provider_type="remote::wolfram-alpha",
config={
"api_key": os.environ["WOLFRAM_ALPHA_API_KEY"],
},
),
],
)
@pytest.fixture(scope="session")
def tool_group_input_memory() -> ToolGroupInput:
return ToolGroupInput(
toolgroup_id="builtin::memory",
provider_id="memory-runtime",
)
@pytest.fixture(scope="session")
def tool_group_input_tavily_search() -> ToolGroupInput:
return ToolGroupInput(
toolgroup_id="builtin::web_search",
provider_id="tavily-search",
)
@pytest.fixture(scope="session")
def tool_group_input_wolfram_alpha() -> ToolGroupInput:
return ToolGroupInput(
toolgroup_id="builtin::wolfram_alpha",
provider_id="wolfram-alpha",
)
TOOL_RUNTIME_FIXTURES = ["memory_and_search"]
@pytest_asyncio.fixture(scope="session")
async def tools_stack(
request,
inference_model,
tool_group_input_memory,
tool_group_input_tavily_search,
tool_group_input_wolfram_alpha,
):
fixture_dict = request.param
providers = {}
provider_data = {}
for key in ["inference", "memory", "tool_runtime"]:
fixture = request.getfixturevalue(f"{key}_{fixture_dict[key]}")
providers[key] = fixture.providers
if key == "inference":
providers[key].append(
Provider(
provider_id="tools_memory_provider",
provider_type="inline::sentence-transformers",
config={},
)
)
if fixture.provider_data:
provider_data.update(fixture.provider_data)
inference_models = (
inference_model if isinstance(inference_model, list) else [inference_model]
)
models = [
ModelInput(
model_id=model,
model_type=ModelType.llm,
provider_id=providers["inference"][0].provider_id,
)
for model in inference_models
]
models.append(
ModelInput(
model_id="all-MiniLM-L6-v2",
model_type=ModelType.embedding,
provider_id="tools_memory_provider",
metadata={"embedding_dimension": 384},
)
)
test_stack = await construct_stack_for_test(
[Api.tool_groups, Api.inference, Api.memory, Api.tool_runtime],
providers,
provider_data,
models=models,
tool_groups=[
tool_group_input_tavily_search,
tool_group_input_wolfram_alpha,
tool_group_input_memory,
],
)
return test_stack

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import os
import pytest
from llama_stack.apis.inference import UserMessage
from llama_stack.apis.memory import MemoryBankDocument
from llama_stack.apis.memory_banks import VectorMemoryBankParams
from llama_stack.apis.tools import ToolInvocationResult
from llama_stack.providers.datatypes import Api
@pytest.fixture
def sample_search_query():
return "What are the latest developments in quantum computing?"
@pytest.fixture
def sample_wolfram_alpha_query():
return "What is the square root of 16?"
@pytest.fixture
def sample_documents():
urls = [
"memory_optimizations.rst",
"chat.rst",
"llama3.rst",
"datasets.rst",
"qat_finetune.rst",
"lora_finetune.rst",
]
return [
MemoryBankDocument(
document_id=f"num-{i}",
content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
mime_type="text/plain",
metadata={},
)
for i, url in enumerate(urls)
]
class TestTools:
@pytest.mark.asyncio
async def test_web_search_tool(self, tools_stack, sample_search_query):
"""Test the web search tool functionality."""
if "TAVILY_SEARCH_API_KEY" not in os.environ:
pytest.skip("TAVILY_SEARCH_API_KEY not set, skipping test")
tools_impl = tools_stack.impls[Api.tool_runtime]
# Execute the tool
response = await tools_impl.invoke_tool(
tool_name="web_search", args={"query": sample_search_query}
)
# Verify the response
assert isinstance(response, ToolInvocationResult)
assert response.content is not None
assert len(response.content) > 0
assert isinstance(response.content, str)
@pytest.mark.asyncio
async def test_wolfram_alpha_tool(self, tools_stack, sample_wolfram_alpha_query):
"""Test the wolfram alpha tool functionality."""
if "WOLFRAM_ALPHA_API_KEY" not in os.environ:
pytest.skip("WOLFRAM_ALPHA_API_KEY not set, skipping test")
tools_impl = tools_stack.impls[Api.tool_runtime]
response = await tools_impl.invoke_tool(
tool_name="wolfram_alpha", args={"query": sample_wolfram_alpha_query}
)
# Verify the response
assert isinstance(response, ToolInvocationResult)
assert response.content is not None
assert len(response.content) > 0
assert isinstance(response.content, str)
@pytest.mark.asyncio
async def test_memory_tool(self, tools_stack, sample_documents):
"""Test the memory tool functionality."""
memory_banks_impl = tools_stack.impls[Api.memory_banks]
memory_impl = tools_stack.impls[Api.memory]
tools_impl = tools_stack.impls[Api.tool_runtime]
# Register memory bank
await memory_banks_impl.register_memory_bank(
memory_bank_id="test_bank",
params=VectorMemoryBankParams(
embedding_model="all-MiniLM-L6-v2",
chunk_size_in_tokens=512,
overlap_size_in_tokens=64,
),
provider_id="faiss",
)
# Insert documents into memory
await memory_impl.insert_documents(
bank_id="test_bank",
documents=sample_documents,
)
# Execute the memory tool
response = await tools_impl.invoke_tool(
tool_name="memory",
args={
"messages": [
UserMessage(
content="What are the main topics covered in the documentation?",
)
],
"memory_bank_ids": ["test_bank"],
},
)
# Verify the response
assert isinstance(response, ToolInvocationResult)
assert response.content is not None
assert len(response.content) > 0