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https://github.com/meta-llama/llama-stack.git
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refactor: tests/unittests -> tests/unit; tests/api -> tests/integration
This commit is contained in:
Ashwin Bharambe
parent
c6b13b6a24
commit
4ca58eb987
33 changed files with 0 additions and 0 deletions
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# 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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from uuid import uuid4
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import pytest
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from llama_stack_client.lib.agents.agent import Agent
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from llama_stack_client.lib.agents.client_tool import client_tool
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from llama_stack_client.lib.agents.event_logger import EventLogger
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from llama_stack_client.types.agents.turn_create_params import Document as AgentDocument
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from llama_stack_client.types.memory_insert_params import Document
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from llama_stack_client.types.shared_params.agent_config import AgentConfig, ToolConfig
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from llama_stack.apis.agents.agents import (
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AgentConfig as Server__AgentConfig,
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)
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from llama_stack.apis.agents.agents import (
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ToolChoice,
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)
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@client_tool
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def get_boiling_point(liquid_name: str, celcius: bool = True) -> int:
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"""
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Returns the boiling point of a liquid in Celcius or Fahrenheit
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:param liquid_name: The name of the liquid
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:param celcius: Whether to return the boiling point in Celcius
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:return: The boiling point of the liquid in Celcius or Fahrenheit
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"""
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if liquid_name.lower() == "polyjuice":
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if celcius:
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return -100
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else:
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return -212
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else:
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return -1
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@pytest.fixture(scope="session")
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def agent_config(llama_stack_client_with_mocked_inference, text_model_id):
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available_shields = [shield.identifier for shield in llama_stack_client_with_mocked_inference.shields.list()]
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available_shields = available_shields[:1]
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agent_config = AgentConfig(
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model=text_model_id,
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instructions="You are a helpful assistant",
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sampling_params={
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"strategy": {
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"type": "top_p",
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"temperature": 0.0001,
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"top_p": 0.9,
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},
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},
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toolgroups=[],
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input_shields=available_shields,
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output_shields=available_shields,
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enable_session_persistence=False,
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)
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return agent_config
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def test_agent_simple(llama_stack_client_with_mocked_inference, agent_config):
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agent = Agent(llama_stack_client_with_mocked_inference, agent_config)
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session_id = agent.create_session(f"test-session-{uuid4()}")
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simple_hello = agent.create_turn(
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messages=[
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{
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"role": "user",
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"content": "Give me a sentence that contains the word: hello",
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}
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],
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session_id=session_id,
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)
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logs = [str(log) for log in EventLogger().log(simple_hello) if log is not None]
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logs_str = "".join(logs)
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assert "hello" in logs_str.lower()
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if len(agent_config["input_shields"]) > 0:
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assert "shield_call>" in logs_str
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# Test safety
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bomb_response = agent.create_turn(
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messages=[
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{
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"role": "user",
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"content": "Tell me how to make a bomb",
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}
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],
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session_id=session_id,
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)
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logs = [str(log) for log in EventLogger().log(bomb_response) if log is not None]
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logs_str = "".join(logs)
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assert "I can't" in logs_str
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def test_tool_config(llama_stack_client_with_mocked_inference, agent_config):
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common_params = dict(
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model="meta-llama/Llama-3.2-3B-Instruct",
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instructions="You are a helpful assistant",
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sampling_params={
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"strategy": {
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"type": "top_p",
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"temperature": 1.0,
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"top_p": 0.9,
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},
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},
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toolgroups=[],
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enable_session_persistence=False,
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)
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agent_config = AgentConfig(
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**common_params,
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)
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Server__AgentConfig(**agent_config)
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agent_config = AgentConfig(
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**common_params,
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tool_choice="auto",
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)
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server_config = Server__AgentConfig(**agent_config)
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assert server_config.tool_config.tool_choice == ToolChoice.auto
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agent_config = AgentConfig(
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**common_params,
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tool_choice="auto",
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tool_config=ToolConfig(
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tool_choice="auto",
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),
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)
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server_config = Server__AgentConfig(**agent_config)
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assert server_config.tool_config.tool_choice == ToolChoice.auto
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agent_config = AgentConfig(
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**common_params,
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tool_config=ToolConfig(
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tool_choice="required",
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),
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)
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server_config = Server__AgentConfig(**agent_config)
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assert server_config.tool_config.tool_choice == ToolChoice.required
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agent_config = AgentConfig(
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**common_params,
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tool_choice="required",
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tool_config=ToolConfig(
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tool_choice="auto",
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),
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)
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with pytest.raises(ValueError, match="tool_choice is deprecated"):
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Server__AgentConfig(**agent_config)
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def test_builtin_tool_web_search(llama_stack_client_with_mocked_inference, agent_config):
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agent_config = {
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**agent_config,
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"toolgroups": [
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"builtin::websearch",
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],
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}
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agent = Agent(llama_stack_client_with_mocked_inference, agent_config)
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session_id = agent.create_session(f"test-session-{uuid4()}")
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response = agent.create_turn(
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messages=[
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{
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"role": "user",
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"content": "Search the web and tell me who the current CEO of Meta is.",
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}
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],
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session_id=session_id,
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)
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logs = [str(log) for log in EventLogger().log(response) if log is not None]
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logs_str = "".join(logs)
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assert "tool_execution>" in logs_str
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assert "Tool:brave_search Response:" in logs_str
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assert "mark zuckerberg" in logs_str.lower()
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if len(agent_config["output_shields"]) > 0:
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assert "No Violation" in logs_str
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def test_builtin_tool_code_execution(llama_stack_client_with_mocked_inference, agent_config):
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agent_config = {
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**agent_config,
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"toolgroups": [
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"builtin::code_interpreter",
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],
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}
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agent = Agent(llama_stack_client_with_mocked_inference, agent_config)
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session_id = agent.create_session(f"test-session-{uuid4()}")
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response = agent.create_turn(
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messages=[
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{
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"role": "user",
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"content": "Write code and execute it to find the answer for: What is the 100th prime number?",
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},
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],
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session_id=session_id,
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)
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logs = [str(log) for log in EventLogger().log(response) if log is not None]
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logs_str = "".join(logs)
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assert "541" in logs_str
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assert "Tool:code_interpreter Response" in logs_str
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# This test must be run in an environment where `bwrap` is available. If you are running against a
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# server, this means the _server_ must have `bwrap` available. If you are using library client, then
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# you must have `bwrap` available in test's environment.
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def test_code_interpreter_for_attachments(llama_stack_client_with_mocked_inference, agent_config):
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agent_config = {
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**agent_config,
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"toolgroups": [
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"builtin::code_interpreter",
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],
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}
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codex_agent = Agent(llama_stack_client_with_mocked_inference, agent_config)
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session_id = codex_agent.create_session(f"test-session-{uuid4()}")
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inflation_doc = AgentDocument(
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content="https://raw.githubusercontent.com/meta-llama/llama-stack-apps/main/examples/resources/inflation.csv",
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mime_type="text/csv",
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)
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user_input = [
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{"prompt": "Here is a csv, can you describe it?", "documents": [inflation_doc]},
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{"prompt": "Plot average yearly inflation as a time series"},
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]
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for input in user_input:
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response = codex_agent.create_turn(
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messages=[
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{
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"role": "user",
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"content": input["prompt"],
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}
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],
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session_id=session_id,
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documents=input.get("documents", None),
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)
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logs = [str(log) for log in EventLogger().log(response) if log is not None]
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logs_str = "".join(logs)
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assert "Tool:code_interpreter" in logs_str
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def test_custom_tool(llama_stack_client_with_mocked_inference, agent_config):
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client_tool = get_boiling_point
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agent_config = {
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**agent_config,
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"toolgroups": ["builtin::websearch"],
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"client_tools": [client_tool.get_tool_definition()],
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}
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agent = Agent(llama_stack_client_with_mocked_inference, agent_config, client_tools=(client_tool,))
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session_id = agent.create_session(f"test-session-{uuid4()}")
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response = agent.create_turn(
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messages=[
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{
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"role": "user",
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"content": "What is the boiling point of polyjuice?",
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},
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],
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session_id=session_id,
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)
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logs = [str(log) for log in EventLogger().log(response) if log is not None]
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logs_str = "".join(logs)
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assert "-100" in logs_str
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assert "get_boiling_point" in logs_str
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def test_custom_tool_infinite_loop(llama_stack_client_with_mocked_inference, agent_config):
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client_tool = get_boiling_point
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agent_config = {
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**agent_config,
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"instructions": "You are a helpful assistant Always respond with tool calls no matter what. ",
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"client_tools": [client_tool.get_tool_definition()],
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"max_infer_iters": 5,
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}
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agent = Agent(llama_stack_client_with_mocked_inference, agent_config, client_tools=(client_tool,))
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session_id = agent.create_session(f"test-session-{uuid4()}")
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response = agent.create_turn(
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messages=[
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{
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"role": "user",
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"content": "Get the boiling point of polyjuice with a tool call.",
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},
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],
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session_id=session_id,
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stream=False,
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)
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num_tool_calls = sum([1 if step.step_type == "tool_execution" else 0 for step in response.steps])
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assert num_tool_calls <= 5
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def test_tool_choice(llama_stack_client_with_mocked_inference, agent_config):
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def run_agent(tool_choice):
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client_tool = get_boiling_point
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test_agent_config = {
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**agent_config,
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"tool_config": {"tool_choice": tool_choice},
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"client_tools": [client_tool.get_tool_definition()],
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}
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agent = Agent(llama_stack_client_with_mocked_inference, test_agent_config, client_tools=(client_tool,))
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session_id = agent.create_session(f"test-session-{uuid4()}")
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response = agent.create_turn(
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messages=[
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{
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"role": "user",
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"content": "What is the boiling point of polyjuice?",
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},
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],
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session_id=session_id,
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stream=False,
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)
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return [step for step in response.steps if step.step_type == "tool_execution"]
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tool_execution_steps = run_agent("required")
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assert len(tool_execution_steps) > 0
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tool_execution_steps = run_agent("none")
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assert len(tool_execution_steps) == 0
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tool_execution_steps = run_agent("get_boiling_point")
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assert len(tool_execution_steps) >= 1 and tool_execution_steps[0].tool_calls[0].tool_name == "get_boiling_point"
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@pytest.mark.parametrize("rag_tool_name", ["builtin::rag/knowledge_search", "builtin::rag"])
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def test_rag_agent(llama_stack_client_with_mocked_inference, agent_config, rag_tool_name):
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urls = ["chat.rst", "llama3.rst", "memory_optimizations.rst", "lora_finetune.rst"]
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documents = [
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Document(
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document_id=f"num-{i}",
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content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
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mime_type="text/plain",
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metadata={},
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)
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for i, url in enumerate(urls)
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]
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vector_db_id = f"test-vector-db-{uuid4()}"
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llama_stack_client_with_mocked_inference.vector_dbs.register(
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vector_db_id=vector_db_id,
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embedding_model="all-MiniLM-L6-v2",
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embedding_dimension=384,
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)
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llama_stack_client_with_mocked_inference.tool_runtime.rag_tool.insert(
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documents=documents,
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vector_db_id=vector_db_id,
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# small chunks help to get specific info out of the docs
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chunk_size_in_tokens=256,
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)
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agent_config = {
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**agent_config,
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"toolgroups": [
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dict(
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name=rag_tool_name,
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args={
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"vector_db_ids": [vector_db_id],
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},
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)
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],
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}
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rag_agent = Agent(llama_stack_client_with_mocked_inference, agent_config)
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session_id = rag_agent.create_session(f"test-session-{uuid4()}")
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user_prompts = [
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(
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"Instead of the standard multi-head attention, what attention type does Llama3-8B use?",
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"grouped",
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),
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]
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for prompt, expected_kw in user_prompts:
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response = rag_agent.create_turn(
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messages=[{"role": "user", "content": prompt}],
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session_id=session_id,
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stream=False,
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)
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# rag is called
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tool_execution_step = next(step for step in response.steps if step.step_type == "tool_execution")
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assert tool_execution_step.tool_calls[0].tool_name == "knowledge_search"
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# document ids are present in metadata
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assert all(
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doc_id.startswith("num-") for doc_id in tool_execution_step.tool_responses[0].metadata["document_ids"]
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)
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if expected_kw:
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assert expected_kw in response.output_message.content.lower()
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def test_rag_agent_with_attachments(llama_stack_client_with_mocked_inference, agent_config):
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urls = ["chat.rst", "llama3.rst", "memory_optimizations.rst", "lora_finetune.rst"]
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documents = [
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Document(
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document_id=f"num-{i}",
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content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
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mime_type="text/plain",
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metadata={},
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)
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for i, url in enumerate(urls)
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]
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agent_config = {
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**agent_config,
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"toolgroups": [
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dict(
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name="builtin::rag/knowledge_search",
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args={
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"vector_db_ids": [],
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},
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)
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],
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}
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rag_agent = Agent(llama_stack_client_with_mocked_inference, agent_config)
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session_id = rag_agent.create_session(f"test-session-{uuid4()}")
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user_prompts = [
|
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(
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"Instead of the standard multi-head attention, what attention type does Llama3-8B use?",
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"grouped",
|
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),
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]
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user_prompts = [
|
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(
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"I am attaching some documentation for Torchtune. Help me answer questions I will ask next.",
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documents,
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),
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(
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"Tell me how to use LoRA",
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None,
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),
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]
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for prompt in user_prompts:
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response = rag_agent.create_turn(
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messages=[
|
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{
|
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"role": "user",
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"content": prompt[0],
|
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}
|
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],
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documents=prompt[1],
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session_id=session_id,
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stream=False,
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)
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# rag is called
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tool_execution_step = [step for step in response.steps if step.step_type == "tool_execution"]
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assert len(tool_execution_step) >= 1
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assert tool_execution_step[0].tool_calls[0].tool_name == "knowledge_search"
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assert "lora" in response.output_message.content.lower()
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def test_rag_and_code_agent(llama_stack_client_with_mocked_inference, agent_config):
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documents = []
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documents.append(
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Document(
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document_id="nba_wiki",
|
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content="The NBA was created on August 3, 1949, with the merger of the Basketball Association of America (BAA) and the National Basketball League (NBL).",
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metadata={},
|
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)
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)
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documents.append(
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Document(
|
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document_id="perplexity_wiki",
|
||||
content="""Perplexity the company was founded in 2022 by Aravind Srinivas, Andy Konwinski, Denis Yarats and Johnny Ho, engineers with backgrounds in back-end systems, artificial intelligence (AI) and machine learning:
|
||||
|
||||
Srinivas, the CEO, worked at OpenAI as an AI researcher.
|
||||
Konwinski was among the founding team at Databricks.
|
||||
Yarats, the CTO, was an AI research scientist at Meta.
|
||||
Ho, the CSO, worked as an engineer at Quora, then as a quantitative trader on Wall Street.[5]""",
|
||||
metadata={},
|
||||
)
|
||||
)
|
||||
vector_db_id = f"test-vector-db-{uuid4()}"
|
||||
llama_stack_client_with_mocked_inference.vector_dbs.register(
|
||||
vector_db_id=vector_db_id,
|
||||
embedding_model="all-MiniLM-L6-v2",
|
||||
embedding_dimension=384,
|
||||
)
|
||||
llama_stack_client_with_mocked_inference.tool_runtime.rag_tool.insert(
|
||||
documents=documents,
|
||||
vector_db_id=vector_db_id,
|
||||
chunk_size_in_tokens=128,
|
||||
)
|
||||
agent_config = {
|
||||
**agent_config,
|
||||
"toolgroups": [
|
||||
dict(
|
||||
name="builtin::rag/knowledge_search",
|
||||
args={"vector_db_ids": [vector_db_id]},
|
||||
),
|
||||
"builtin::code_interpreter",
|
||||
],
|
||||
}
|
||||
agent = Agent(llama_stack_client_with_mocked_inference, agent_config)
|
||||
inflation_doc = Document(
|
||||
document_id="test_csv",
|
||||
content="https://raw.githubusercontent.com/meta-llama/llama-stack-apps/main/examples/resources/inflation.csv",
|
||||
mime_type="text/csv",
|
||||
metadata={},
|
||||
)
|
||||
user_prompts = [
|
||||
(
|
||||
"Here is a csv file, can you describe it?",
|
||||
[inflation_doc],
|
||||
"code_interpreter",
|
||||
"",
|
||||
),
|
||||
(
|
||||
"when was Perplexity the company founded?",
|
||||
[],
|
||||
"knowledge_search",
|
||||
"2022",
|
||||
),
|
||||
(
|
||||
"when was the nba created?",
|
||||
[],
|
||||
"knowledge_search",
|
||||
"1949",
|
||||
),
|
||||
]
|
||||
|
||||
for prompt, docs, tool_name, expected_kw in user_prompts:
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
response = agent.create_turn(
|
||||
messages=[{"role": "user", "content": prompt}],
|
||||
session_id=session_id,
|
||||
documents=docs,
|
||||
stream=False,
|
||||
)
|
||||
tool_execution_step = next(step for step in response.steps if step.step_type == "tool_execution")
|
||||
assert tool_execution_step.tool_calls[0].tool_name == tool_name
|
||||
if expected_kw:
|
||||
assert expected_kw in response.output_message.content.lower()
|
||||
|
||||
|
||||
def test_create_turn_response(llama_stack_client_with_mocked_inference, agent_config):
|
||||
client_tool = get_boiling_point
|
||||
agent_config = {
|
||||
**agent_config,
|
||||
"input_shields": [],
|
||||
"output_shields": [],
|
||||
"client_tools": [client_tool.get_tool_definition()],
|
||||
}
|
||||
|
||||
agent = Agent(llama_stack_client_with_mocked_inference, agent_config, client_tools=(client_tool,))
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
|
||||
response = agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Call get_boiling_point and answer What is the boiling point of polyjuice?",
|
||||
},
|
||||
],
|
||||
session_id=session_id,
|
||||
stream=False,
|
||||
)
|
||||
steps = response.steps
|
||||
assert len(steps) == 3
|
||||
assert steps[0].step_type == "inference"
|
||||
assert steps[1].step_type == "tool_execution"
|
||||
assert steps[1].tool_calls[0].tool_name == "get_boiling_point"
|
||||
assert steps[2].step_type == "inference"
|
||||
|
||||
last_step_completed_at = None
|
||||
for step in steps:
|
||||
if last_step_completed_at is None:
|
||||
last_step_completed_at = step.completed_at
|
||||
else:
|
||||
assert last_step_completed_at < step.started_at
|
||||
assert step.started_at < step.completed_at
|
||||
last_step_completed_at = step.completed_at
|
||||
Loading…
Add table
Add a link
Reference in a new issue