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https://github.com/agentscope-ai/ReMe.git
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e31db5fe19
* docs: rename vault_dir to workspace_dir in documentation and examples * refactor(extract): format long method call across multiple lines * refactor(extract): format system prompt parameters for better readability
118 lines
4.6 KiB
Python
118 lines
4.6 KiB
Python
"""Integration tests: drive Agent through LLMDemoStep + Application wiring.
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Requires LLM_API_KEY (and optionally LLM_BASE_URL / LLM_MODEL_NAME) in the
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environment or a .env file at the repo root. Hits the real Anthropic API.
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"""
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import asyncio
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import sys
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from pathlib import Path
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from typing import Literal
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from pydantic import BaseModel, Field
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INTEGRATION_DIR = Path(__file__).resolve().parent
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sys.path.insert(0, str(INTEGRATION_DIR))
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# pylint: disable=wrong-import-position
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from _workspace_fixture import workspace_env # noqa: E402
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from reme.steps.common.llm_demo import LLMDemoStep # noqa: E402
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class MathResult(BaseModel):
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"""Structured output for a math computation."""
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expression: str = Field(description="The math expression that was evaluated")
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result: float = Field(description="The numeric result")
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explanation: str = Field(description="Brief explanation of the computation")
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class SentimentAnalysis(BaseModel):
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"""Structured output for sentiment analysis."""
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sentiment: Literal["positive", "negative", "neutral"] = Field(
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description="The overall sentiment of the text",
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)
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confidence: float = Field(
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description="Confidence score between 0 and 1",
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)
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key_phrases: list[str] = Field(
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description="Key phrases that indicate the sentiment",
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)
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async def _run_basic_chat(app) -> None:
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step = LLMDemoStep(app_context=app.context)
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response = await step(
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query="What is 1 + 1? Reply with just the number.",
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)
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text = (response.answer or "").strip()
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print(f"\n[basic_chat] response: {text!r}")
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assert text, "Empty assistant response"
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assert "2" in text, f"Expected '2' in response, got: {text!r}"
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print("✓ test_llm_demo_step_basic_chat passed")
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async def _run_with_tool(app) -> None:
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step = LLMDemoStep(app_context=app.context)
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response = await step(
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query="Use the add tool to compute 21 + 21 and report the result.",
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sys_prompt="Use the `add` tool whenever the user asks to add numbers.",
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)
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text = (response.answer or "").strip()
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print(f"\n[with_tool] response: {text!r}")
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assert "42" in text, f"Expected '42' in response, got: {text!r}"
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print("✓ test_llm_demo_step_with_tool passed")
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async def _run_structured_output(app) -> None:
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step = LLMDemoStep(app_context=app.context)
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response = await step(
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query="What is 15 multiplied by 7? Show your work.",
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sys_prompt="You are a math tutor. Solve the problem step by step.",
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structured_model=MathResult,
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)
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structured = response.metadata.get("structured_output")
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print(f"\n[structured_output] result: {structured}")
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assert structured is not None, "structured_output should not be None"
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assert "result" in structured, "structured_output should have 'result' field"
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assert structured["result"] == 105, f"Expected result=105, got: {structured['result']}"
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assert "expression" in structured, "structured_output should have 'expression' field"
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assert "explanation" in structured, "structured_output should have 'explanation' field"
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print("✓ test_llm_demo_step_structured_output passed")
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async def _run_structured_output_enum(app) -> None:
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step = LLMDemoStep(app_context=app.context)
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response = await step(
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query="Analyze the sentiment: 'I absolutely love this product! It exceeded all my expectations.'",
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sys_prompt="You are a sentiment analysis expert. Analyze the given text.",
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structured_model=SentimentAnalysis,
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)
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structured = response.metadata.get("structured_output")
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print(f"\n[structured_enum] result: {structured}")
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assert structured is not None, "structured_output should not be None"
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assert structured["sentiment"] == "positive", f"Expected sentiment='positive', got: {structured['sentiment']}"
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assert 0 <= structured["confidence"] <= 1, f"Confidence should be 0-1, got: {structured['confidence']}"
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assert isinstance(structured["key_phrases"], list), "key_phrases should be a list"
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assert len(structured["key_phrases"]) > 0, "key_phrases should not be empty"
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print("✓ test_llm_demo_step_structured_output_enum passed")
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async def _run_all() -> None:
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with workspace_env() as env:
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app = await env.make_app()
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try:
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await _run_basic_chat(app)
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await _run_with_tool(app)
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await _run_structured_output(app)
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await _run_structured_output_enum(app)
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finally:
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await env.close_all()
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if __name__ == "__main__":
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print("=== LLMDemoStep + Agent integration tests ===")
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asyncio.run(_run_all())
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print("\nAll integration tests passed!")
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