330 lines
11 KiB
Python
330 lines
11 KiB
Python
import json
|
||
import os
|
||
import threading
|
||
import time
|
||
import uuid
|
||
from typing import List
|
||
from unittest.mock import MagicMock, patch
|
||
|
||
import pytest
|
||
import requests
|
||
from dotenv import load_dotenv
|
||
from letta_client import Letta, MessageCreate
|
||
from letta_client.types import ToolReturnMessage
|
||
|
||
from letta.schemas.agent import AgentState
|
||
from letta.schemas.llm_config import LLMConfig
|
||
from letta.services.tool_executor.builtin_tool_executor import LettaBuiltinToolExecutor
|
||
from letta.settings import tool_settings
|
||
|
||
# ------------------------------
|
||
# Fixtures
|
||
# ------------------------------
|
||
|
||
|
||
@pytest.fixture(scope="module")
|
||
def server_url() -> str:
|
||
"""
|
||
Provides the URL for the Letta server.
|
||
If LETTA_SERVER_URL is not set, starts the server in a background thread
|
||
and polls until it’s accepting connections.
|
||
"""
|
||
|
||
def _run_server() -> None:
|
||
load_dotenv()
|
||
from letta.server.rest_api.app import start_server
|
||
|
||
start_server(debug=True)
|
||
|
||
url: str = os.getenv("LETTA_SERVER_URL", "http://localhost:8283")
|
||
|
||
if not os.getenv("LETTA_SERVER_URL"):
|
||
thread = threading.Thread(target=_run_server, daemon=True)
|
||
thread.start()
|
||
|
||
# Poll until the server is up (or timeout)
|
||
timeout_seconds = 30
|
||
deadline = time.time() + timeout_seconds
|
||
while time.time() < deadline:
|
||
try:
|
||
resp = requests.get(url + "/v1/health")
|
||
if resp.status_code < 500:
|
||
break
|
||
except requests.exceptions.RequestException:
|
||
pass
|
||
time.sleep(0.1)
|
||
else:
|
||
raise RuntimeError(f"Could not reach {url} within {timeout_seconds}s")
|
||
|
||
yield url
|
||
|
||
|
||
@pytest.fixture(scope="module")
|
||
def client(server_url: str) -> Letta:
|
||
"""
|
||
Creates and returns a synchronous Letta REST client for testing.
|
||
"""
|
||
client_instance = Letta(base_url=server_url)
|
||
yield client_instance
|
||
|
||
|
||
@pytest.fixture(scope="function")
|
||
def agent_state(client: Letta) -> AgentState:
|
||
"""
|
||
Creates and returns an agent state for testing with a pre-configured agent.
|
||
Uses system-level EXA_API_KEY setting.
|
||
"""
|
||
client.tools.upsert_base_tools()
|
||
|
||
send_message_tool = client.tools.list(name="send_message")[0]
|
||
run_code_tool = client.tools.list(name="run_code")[0]
|
||
web_search_tool = client.tools.list(name="web_search")[0]
|
||
agent_state_instance = client.agents.create(
|
||
name="test_builtin_tools_agent",
|
||
include_base_tools=False,
|
||
tool_ids=[send_message_tool.id, run_code_tool.id, web_search_tool.id],
|
||
model="openai/gpt-4o",
|
||
embedding="letta/letta-free",
|
||
tags=["test_builtin_tools_agent"],
|
||
)
|
||
yield agent_state_instance
|
||
|
||
|
||
# ------------------------------
|
||
# Helper Functions and Constants
|
||
# ------------------------------
|
||
|
||
|
||
def get_llm_config(filename: str, llm_config_dir: str = "tests/configs/llm_model_configs") -> LLMConfig:
|
||
filename = os.path.join(llm_config_dir, filename)
|
||
with open(filename, "r") as f:
|
||
config_data = json.load(f)
|
||
llm_config = LLMConfig(**config_data)
|
||
return llm_config
|
||
|
||
|
||
USER_MESSAGE_OTID = str(uuid.uuid4())
|
||
all_configs = [
|
||
"openai-gpt-4o-mini.json",
|
||
]
|
||
requested = os.getenv("LLM_CONFIG_FILE")
|
||
filenames = [requested] if requested else all_configs
|
||
TESTED_LLM_CONFIGS: List[LLMConfig] = [get_llm_config(fn) for fn in filenames]
|
||
|
||
TEST_LANGUAGES = ["Python", "Javascript", "Typescript"]
|
||
EXPECTED_INTEGER_PARTITION_OUTPUT = "190569292"
|
||
|
||
|
||
# Reference implementation in Python, to embed in the user prompt
|
||
REFERENCE_CODE = """\
|
||
def reference_partition(n):
|
||
partitions = [1] + [0] * (n + 1)
|
||
for k in range(1, n + 1):
|
||
for i in range(k, n + 1):
|
||
partitions[i] += partitions[i - k]
|
||
return partitions[n]
|
||
"""
|
||
|
||
|
||
def reference_partition(n: int) -> int:
|
||
# Same logic, used to compute expected result in the test
|
||
partitions = [1] + [0] * (n + 1)
|
||
for k in range(1, n + 1):
|
||
for i in range(k, n + 1):
|
||
partitions[i] += partitions[i - k]
|
||
return partitions[n]
|
||
|
||
|
||
# ------------------------------
|
||
# Test Cases
|
||
# ------------------------------
|
||
|
||
|
||
@pytest.mark.parametrize("language", TEST_LANGUAGES, ids=TEST_LANGUAGES)
|
||
def test_run_code(
|
||
client: Letta,
|
||
agent_state: AgentState,
|
||
language: str,
|
||
) -> None:
|
||
"""
|
||
Sends a reference Python implementation, asks the model to translate & run it
|
||
in different languages, and verifies the exact partition(100) result.
|
||
"""
|
||
expected = str(reference_partition(100))
|
||
|
||
user_message = MessageCreate(
|
||
role="user",
|
||
content=(
|
||
"Here is a Python reference implementation:\n\n"
|
||
f"{REFERENCE_CODE}\n"
|
||
f"Please translate and execute this code in {language} to compute p(100), "
|
||
"and return **only** the result with no extra formatting."
|
||
),
|
||
otid=USER_MESSAGE_OTID,
|
||
)
|
||
|
||
response = client.agents.messages.create(
|
||
agent_id=agent_state.id,
|
||
messages=[user_message],
|
||
)
|
||
|
||
tool_returns = [m for m in response.messages if isinstance(m, ToolReturnMessage)]
|
||
assert tool_returns, f"No ToolReturnMessage found for language: {language}"
|
||
|
||
returns = [m.tool_return for m in tool_returns]
|
||
assert any(expected in ret for ret in returns), (
|
||
f"For language={language!r}, expected to find '{expected}' in tool_return, but got {returns!r}"
|
||
)
|
||
|
||
|
||
@patch("exa_py.Exa")
|
||
def test_web_search(
|
||
mock_exa_class,
|
||
client: Letta,
|
||
agent_state: AgentState,
|
||
) -> None:
|
||
# Mock Exa search result with education information
|
||
mock_exa_result = MagicMock()
|
||
mock_exa_result.results = [
|
||
MagicMock(
|
||
title="Charles Packer - UC Berkeley PhD in Computer Science",
|
||
url="https://example.com/charles-packer-profile",
|
||
published_date="2023-01-01",
|
||
author="UC Berkeley",
|
||
text=None, # include_text=False by default
|
||
highlights=["Charles Packer completed his PhD at UC Berkeley", "Research in artificial intelligence and machine learning"],
|
||
summary="Charles Packer is the CEO of Letta who earned his PhD in Computer Science from UC Berkeley, specializing in AI research.",
|
||
),
|
||
MagicMock(
|
||
title="Letta Leadership Team",
|
||
url="https://letta.com/team",
|
||
published_date="2023-06-01",
|
||
author="Letta",
|
||
text=None,
|
||
highlights=["CEO Charles Packer brings academic expertise"],
|
||
summary="Leadership team page featuring CEO Charles Packer's educational background.",
|
||
),
|
||
]
|
||
|
||
# Setup mock
|
||
mock_exa_client = MagicMock()
|
||
mock_exa_class.return_value = mock_exa_client
|
||
mock_exa_client.search_and_contents.return_value = mock_exa_result
|
||
|
||
user_message = MessageCreate(
|
||
role="user",
|
||
content="I am executing a test. Use the web search tool to find where I, Charles Packer, the CEO of Letta, went to school.",
|
||
otid=USER_MESSAGE_OTID,
|
||
)
|
||
|
||
response = client.agents.messages.create(
|
||
agent_id=agent_state.id,
|
||
messages=[user_message],
|
||
)
|
||
|
||
tool_returns = [m for m in response.messages if isinstance(m, ToolReturnMessage)]
|
||
assert tool_returns, "No ToolReturnMessage found"
|
||
|
||
returns = [m.tool_return for m in tool_returns]
|
||
print(returns)
|
||
|
||
# Parse the JSON response from web_search
|
||
assert len(returns) > 0, "No tool returns found"
|
||
response_json = json.loads(returns[0])
|
||
|
||
# Basic structure assertions for new Exa format
|
||
assert "query" in response_json, "Missing 'query' field in response"
|
||
assert "results" in response_json, "Missing 'results' field in response"
|
||
|
||
# Verify we got search results
|
||
results = response_json["results"]
|
||
assert len(results) == 2, "Should have found exactly 2 search results from mock"
|
||
|
||
# Check each result has the expected structure
|
||
found_education_info = False
|
||
for result in results:
|
||
assert "title" in result, "Result missing title"
|
||
assert "url" in result, "Result missing URL"
|
||
|
||
# text should not be present since include_text=False by default
|
||
assert "text" not in result or result["text"] is None, "Text should not be included by default"
|
||
|
||
# Check for education-related information in summary and highlights
|
||
result_text = ""
|
||
if "summary" in result and result["summary"]:
|
||
result_text += " " + result["summary"].lower()
|
||
if "highlights" in result and result["highlights"]:
|
||
for highlight in result["highlights"]:
|
||
result_text += " " + highlight.lower()
|
||
|
||
# Look for education keywords
|
||
if any(keyword in result_text for keyword in ["berkeley", "university", "phd", "ph.d", "education", "student"]):
|
||
found_education_info = True
|
||
|
||
assert found_education_info, "Should have found education-related information about Charles Packer"
|
||
|
||
# Verify Exa was called with correct parameters
|
||
mock_exa_client.search_and_contents.assert_called_once()
|
||
call_args = mock_exa_client.search_and_contents.call_args
|
||
assert call_args[1]["type"] == "auto"
|
||
assert call_args[1]["text"] is False # Default is False now
|
||
|
||
|
||
@pytest.mark.asyncio(scope="function")
|
||
async def test_web_search_uses_exa():
|
||
"""Test that web search uses Exa API correctly."""
|
||
|
||
# create mock agent state with exa api key
|
||
mock_agent_state = MagicMock()
|
||
mock_agent_state.get_agent_env_vars_as_dict.return_value = {"EXA_API_KEY": "test-exa-key"}
|
||
|
||
# Mock exa search result
|
||
mock_exa_result = MagicMock()
|
||
mock_exa_result.results = [
|
||
MagicMock(
|
||
title="Test Result",
|
||
url="https://example.com/test",
|
||
published_date="2023-01-01",
|
||
author="Test Author",
|
||
text="This is test content from the search result.",
|
||
highlights=["This is a highlight"],
|
||
summary="This is a summary of the content.",
|
||
)
|
||
]
|
||
|
||
with patch("exa_py.Exa") as mock_exa_class:
|
||
# Mock Exa
|
||
mock_exa_client = MagicMock()
|
||
mock_exa_class.return_value = mock_exa_client
|
||
mock_exa_client.search_and_contents.return_value = mock_exa_result
|
||
|
||
# create executor with mock dependencies
|
||
executor = LettaBuiltinToolExecutor(
|
||
message_manager=MagicMock(),
|
||
agent_manager=MagicMock(),
|
||
block_manager=MagicMock(),
|
||
run_manager=MagicMock(),
|
||
passage_manager=MagicMock(),
|
||
actor=MagicMock(),
|
||
)
|
||
|
||
result = await executor.web_search(agent_state=mock_agent_state, query="test query", num_results=3, include_text=True)
|
||
|
||
# Verify Exa was called correctly
|
||
mock_exa_class.assert_called_once_with(api_key="test-exa-key")
|
||
mock_exa_client.search_and_contents.assert_called_once()
|
||
|
||
# Check the call arguments
|
||
call_args = mock_exa_client.search_and_contents.call_args
|
||
assert call_args[1]["query"] == "test query"
|
||
assert call_args[1]["num_results"] == 3
|
||
assert call_args[1]["type"] == "auto"
|
||
assert call_args[1]["text"] == True
|
||
|
||
# Verify the response format
|
||
response_json = json.loads(result)
|
||
assert "query" in response_json
|
||
assert "results" in response_json
|
||
assert response_json["query"] == "test query"
|
||
assert len(response_json["results"]) == 1
|