699 lines
26 KiB
Python
699 lines
26 KiB
Python
"""
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Honcho Test Runner
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A script that executes JSON-formatted tests against a running Honcho instance.
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This script:
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1. Loads test definitions from JSON files
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2. Creates a workspace for each test
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3. Adds all messages to sessions
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4. Waits for the deriver queue to be empty
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5. Executes queries and judges responses using an LLM
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"""
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import argparse
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import asyncio
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import json
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import logging
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import os
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import sys
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import time
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from pathlib import Path
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from typing import Any
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import tiktoken
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from anthropic import AsyncAnthropic
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from dotenv import load_dotenv
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from honcho import Honcho
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from honcho.session import SessionPeerConfig
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from typing_extensions import TypedDict
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load_dotenv()
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class SessionResult(TypedDict):
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"""Type definition for session creation results."""
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name: str
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message_count: int
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class QueryResult(TypedDict):
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"""Type definition for query execution results."""
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query: str
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expected_response: str
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actual_response: str
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session: str | None
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observer: str | None
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target: str | None
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judgment: dict[str, Any]
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class TestResult(TypedDict):
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"""Type definition for test execution results."""
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test_name: str
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workspace_id: str
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sessions_created: list[SessionResult]
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queries_executed: list[QueryResult]
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passed: bool
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error: str | None
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start_time: float
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end_time: float
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duration_seconds: float
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class TestRunner:
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"""
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Executes JSON tests against a Honcho instance.
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"""
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def __init__(
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self,
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honcho_url: str = "http://localhost:8000",
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anthropic_api_key: str | None = None,
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):
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"""
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Initialize the test runner.
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Args:
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honcho_url: URL of the running Honcho instance
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anthropic_api_key: Anthropic API key for judging responses
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"""
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self.honcho_url: str = honcho_url
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self.anthropic_api_key: str | None = anthropic_api_key
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# Configure logging
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logging.basicConfig(
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level=logging.WARNING, format="%(asctime)s - %(levelname)s - %(message)s"
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)
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self.logger: logging.Logger = logging.getLogger(__name__)
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# Suppress HTTP request logs from the Honcho SDK
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logging.getLogger("httpx").setLevel(logging.ERROR)
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logging.getLogger("httpcore").setLevel(logging.ERROR)
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if self.anthropic_api_key:
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self.anthropic_client: AsyncAnthropic = AsyncAnthropic(
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api_key=self.anthropic_api_key
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)
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else:
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api_key = os.getenv("LLM_ANTHROPIC_API_KEY")
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if not api_key:
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raise ValueError("LLM_ANTHROPIC_API_KEY is not set")
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self.anthropic_client = AsyncAnthropic(api_key=api_key)
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def load_test_file(self, test_file: Path) -> dict[str, Any]:
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"""
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Load a test definition from a JSON file.
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Args:
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test_file: Path to the JSON test file
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Returns:
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Test definition dictionary
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"""
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with open(test_file) as f:
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return json.load(f)
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def create_honcho_client(self, workspace_id: str) -> Honcho:
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"""
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Create a Honcho client for a specific workspace.
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Args:
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workspace_id: Workspace ID for the test
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Returns:
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Honcho client instance
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"""
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return Honcho(
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environment="local", workspace_id=workspace_id, base_url=self.honcho_url
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)
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async def wait_for_deriver_queue_empty(
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self, honcho_client: Honcho, session_id: str | None = None
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) -> bool:
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"""
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Wait for the deriver queue to be empty.
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Args:
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honcho_client: Honcho client instance
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timeout: Maximum time to wait in seconds
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Returns:
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True if queue is empty, False if timeout exceeded
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"""
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time.sleep(1)
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try:
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while True:
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status = honcho_client.get_deriver_status(session_id=session_id)
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if (
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status.in_progress_work_units == 0
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and status.pending_work_units == 0
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):
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break
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time.sleep(0.5)
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return True
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except Exception as e:
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self.logger.warning(f"Error polling deriver status: {e}")
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return False
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async def judge_response(
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self, query: str, expected_response: str, actual_response: str
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) -> dict[str, Any]:
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"""
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Use an LLM to judge if the actual response matches the expected response.
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Args:
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query: The original query
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expected_response: Expected response from the test
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actual_response: Actual response from Honcho
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Returns:
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Judgment result with pass/fail and reasoning
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"""
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try:
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system_prompt = """
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You are an expert judge evaluating AI responses. Your task is to determine if an actual response contains the core correct information from an expected response.
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CRITICAL JUDGING PRINCIPLES:
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1. SEMANTIC UNDERSTANDING: Focus on whether the actual response conveys the same core factual information as expected, even if expressed differently
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2. FLEXIBLE INTERPRETATION: Accept responses that are longer, more detailed, or use different phrasing as long as they contain the correct core facts
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3. CONTEXTUAL REASONING: If the response shows logical reasoning that leads to the correct conclusion, consider it correct even if the path differs
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4. CONFLICTING INFORMATION: If a response acknowledges conflicts but correctly identifies the most recent/authoritative information, that should pass
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5. IMPLICIT vs EXPLICIT: Accept responses that imply the correct answer through reasoning, not just explicit statements
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ONLY FAIL when:
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- Core factual information is demonstrably wrong
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- The response contradicts the expected information without justification
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- Essential information is completely missing with no reasonable inference path
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Always respond with valid JSON: {"passed": boolean, "reasoning": "short (1-3 sentences) explanation of why the response is correct or incorrect"}"""
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user_prompt = f"""Query: "{query}"
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Expected response: "{expected_response}"
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Actual response: "{actual_response}"
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Evaluate whether the actual response contains the core correct information from the expected response. Focus on semantic meaning and logical conclusions, not exact phrasing.
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"""
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response = await self.anthropic_client.messages.create(
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model="claude-3-7-sonnet-20250219",
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max_tokens=300,
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temperature=0.0,
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system=system_prompt,
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messages=[
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{
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"role": "user",
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"content": user_prompt,
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}
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],
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)
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if not response.content:
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raise ValueError("Anthropic returned empty response")
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content_block = response.content[0]
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judgment_text = getattr(content_block, "text", None)
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if judgment_text is None:
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raise ValueError(
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f"No text content in response block: {type(content_block)}"
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)
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if judgment_text is None:
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raise ValueError("Anthropic returned empty response")
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# Extract JSON from the response if it's wrapped in markdown
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if "```json" in judgment_text:
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json_start = judgment_text.find("```json") + 7
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json_end = judgment_text.find("```", json_start)
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judgment_text = judgment_text[json_start:json_end].strip()
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elif "```" in judgment_text:
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json_start = judgment_text.find("```") + 3
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json_end = judgment_text.find("```", json_start)
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judgment_text = judgment_text[json_start:json_end].strip()
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judgment = json.loads(judgment_text)
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return judgment
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except Exception as e:
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self.logger.error(f"Error judging response: {e}")
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# Fallback to simple string matching
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is_correct = expected_response.lower() in actual_response.lower()
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return {
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"passed": is_correct,
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"reasoning": f"Fallback string matching due to error: {'Match found' if is_correct else 'No match found'}",
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}
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async def execute_test(self, test_file: Path) -> TestResult:
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"""
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Execute a single test file.
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Args:
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test_file: Path to the JSON test file
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Returns:
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Test execution results
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"""
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test_name = test_file.stem
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print(f"\033[1mExecuting test {test_name}\033[0m")
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# Load test definition
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test_def = self.load_test_file(test_file)
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# Create workspace for this test
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workspace_id = f"test_{test_name}_{int(time.time())}"
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honcho_client = self.create_honcho_client(workspace_id)
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results: TestResult = {
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"test_name": test_name,
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"workspace_id": workspace_id,
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"sessions_created": [],
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"queries_executed": [],
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"passed": False,
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"error": None,
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"start_time": time.time(),
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"end_time": 0.0,
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"duration_seconds": 0.0,
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}
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try:
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# Step 1: Create sessions and add messages
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sessions = test_def.get("sessions", {})
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# Collect all unique peers from messages and queries
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all_peers: set[str] = set()
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for session_data in sessions.values():
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for msg in session_data.get("messages", []):
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all_peers.add(msg["peer"])
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# Get queries and collect peers from them
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queries: list[dict[str, Any]] = test_def.get("queries", [])
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for query_dict in queries:
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if "observer" in query_dict:
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all_peers.add(str(query_dict["observer"]))
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if "target" in query_dict:
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all_peers.add(str(query_dict["target"]))
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# Identify peers that are observers in queries (need observe_others=True)
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observer_peers: set[str] = set()
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for query_dict in queries:
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if "observer" in query_dict and "target" in query_dict:
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observer_peers.add(str(query_dict["observer"]))
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# for efficiency, identify peers that are never targets in any query
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# so we can turn off their observe_me flag
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observed_peers: set[str] = set()
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for query_dict in queries:
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if "target" in query_dict:
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observed_peers.add(str(query_dict["target"]))
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if "observer" in query_dict and "target" not in query_dict:
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observed_peers.add(str(query_dict["observer"]))
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non_observed_peers: set[str] = all_peers - observed_peers
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# Create all peers first
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peers: dict[str, Any] = {}
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for peer_name in all_peers:
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peers[peer_name] = honcho_client.peer(id=peer_name)
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for session_name, session_data in sessions.items():
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# Create session
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session = honcho_client.session(id=str(session_name))
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print(f"\n session: {session_name}")
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# Create peer configurations based on requirements
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peer_configs: list[tuple[Any, SessionPeerConfig]] = []
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for peer_name in all_peers:
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# If this peer is an observer in any *targeted* query, they need to observe others
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if peer_name in observer_peers:
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if peer_name in non_observed_peers:
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config = SessionPeerConfig(
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observe_me=False, observe_others=True
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)
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peer_configs.append((peers[peer_name], config))
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print(f" peer config: {peer_name} -> {config}")
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else:
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config = SessionPeerConfig(
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observe_me=True, observe_others=True
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)
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peer_configs.append((peers[peer_name], config))
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print(f" peer config: {peer_name} -> {config}")
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elif peer_name in observed_peers:
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config = SessionPeerConfig(
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observe_me=True, observe_others=False
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)
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peer_configs.append((peers[peer_name], config))
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print(f" peer config: {peer_name} -> {config}")
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else:
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config = SessionPeerConfig(
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observe_me=False, observe_others=False
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)
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peer_configs.append((peers[peer_name], config))
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print(f" peer config: {peer_name} -> {config}")
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session.add_peers(peer_configs)
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# Add messages to session
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messages = session_data.get("messages", [])
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for msg in messages:
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peer_name: str = msg["peer"]
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content: str = msg["content"]
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truncated_content = (
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content[:140] + "..." if len(content) > 140 else content
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)
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print(f" {peer_name}: {truncated_content}")
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# Add messages to session
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session.add_messages(
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[peers[msg["peer"]].message(msg["content"]) for msg in messages]
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)
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results["sessions_created"].append(
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SessionResult(name=str(session_name), message_count=len(messages))
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)
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# Step 2: Execute queries
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all_queries_passed = True
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for i, query_data in enumerate(queries):
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query: str = query_data["query"]
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expected_response: str = query_data["expected_response"]
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session_name: str | None = query_data.get("session")
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observer_name: str | None = query_data.get("observer")
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target_name: str | None = query_data.get("target")
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# Wait for deriver queue to be empty for this session
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queue_empty = await self.wait_for_deriver_queue_empty(
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honcho_client, session_id=session_name
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)
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if not queue_empty:
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print(f"Deriver queue never emptied for session {session_name}!!!")
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sys.exit(1)
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print(f"\n query {i + 1}: {query}")
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context_parts: list[str] = []
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if session_name:
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context_parts.append(f"session: {session_name}")
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if observer_name:
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context_parts.append(f"observer: {observer_name}")
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if target_name:
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context_parts.append(f"target: {target_name}")
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if context_parts:
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print(" " + ", ".join(context_parts))
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try:
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# Determine which peer to use for the query (observer)
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if observer_name:
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query_peer = peers[observer_name]
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else:
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# Use the first peer from the first session
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first_session_data = list(sessions.values())[0]
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first_peer_name: str = first_session_data["messages"][0]["peer"]
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query_peer = peers[first_peer_name]
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# Execute chat query
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if session_name and target_name:
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response = query_peer.chat(
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query, session_id=session_name, target=peers[target_name]
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)
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elif session_name:
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response = query_peer.chat(query, session_id=session_name)
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elif target_name:
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response = query_peer.chat(query, target=peers[target_name])
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else:
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response = query_peer.chat(query)
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actual_response: str = (
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response.content
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if hasattr(response, "content")
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else str(response)
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)
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# Judge the response
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judgment = await self.judge_response(
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query, expected_response, actual_response
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)
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query_result: QueryResult = {
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"query": query,
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"expected_response": expected_response,
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"actual_response": actual_response,
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"session": session_name,
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"observer": observer_name,
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"target": target_name,
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"judgment": judgment,
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}
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results["queries_executed"].append(query_result)
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print(
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" judgment: \033[1m\033[32mPASS\033[0m"
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if judgment["passed"]
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else " judgment: \033[1m\033[31mFAIL\033[0m"
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)
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if not judgment["passed"]:
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# if failed, print
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print(f" got response: \033[3m{actual_response}\033[0m")
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print(f" expected: {expected_response}")
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else:
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# if passed, just log
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self.logger.info(
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f" got response: \033[3m{actual_response}\033[0m"
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)
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self.logger.info(f" expected: {expected_response}")
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print(f" reasoning: {judgment['reasoning']}")
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# Track if all queries pass
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if not judgment["passed"]:
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all_queries_passed = False
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except Exception as e:
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self.logger.error(f"Error executing query {i + 1}: {e}")
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query_result = QueryResult(
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query=query,
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expected_response=expected_response,
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actual_response=f"ERROR: {e}",
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session=session_name,
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observer=observer_name,
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target=target_name,
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judgment={
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"passed": False,
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"reasoning": f"Query execution failed: {e}",
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},
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)
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results["queries_executed"].append(query_result)
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all_queries_passed = False
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# Step 3: Execute get_context calls
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get_context_calls = test_def.get("get_context_calls", [])
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for i, get_context_call in enumerate(get_context_calls):
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print(f"\n get_context call #{i + 1}")
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session_name = str(get_context_call["session"])
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summary = get_context_call["summary"]
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max_tokens: int | None = get_context_call.get("max_tokens")
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session = honcho_client.session(id=session_name)
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# Wait for deriver queue to be empty for this session
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queue_empty = await self.wait_for_deriver_queue_empty(
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honcho_client, session_id=session_name
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)
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if not queue_empty:
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print(f"Deriver queue never emptied for session {session_name}!!!")
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sys.exit(1)
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session_context = session.get_context(
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summary=summary, tokens=max_tokens
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)
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tokenizer = tiktoken.get_encoding("cl100k_base")
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summary_tokens = len(tokenizer.encode(session_context.summary))
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print(f" summary: {session_context.summary}")
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got_tokens = summary_tokens
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for message in session_context.messages:
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got_tokens += message.token_count
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print(f" max tokens: {max_tokens}")
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print(
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f" got token count: {got_tokens} (summary: {summary_tokens}, messages: {got_tokens - summary_tokens} in {len(session_context.messages)} messages)"
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)
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if max_tokens and got_tokens > max_tokens:
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all_queries_passed = False
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# Determine if test passed (all queries and get_context calls must pass)
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results["passed"] = all_queries_passed
|
|
results["end_time"] = time.time()
|
|
results["duration_seconds"] = results["end_time"] - results["start_time"]
|
|
|
|
print(
|
|
f"\nTest {test_name} completed. Status: {'PASS' if results['passed'] else 'FAIL'} (Duration: {results['duration_seconds']:.2f}s)"
|
|
)
|
|
|
|
except Exception as e:
|
|
self.logger.error(f"Error executing test {test_name}: {e}")
|
|
results["error"] = str(e)
|
|
results["passed"] = False
|
|
results["end_time"] = time.time()
|
|
results["duration_seconds"] = results["end_time"] - results["start_time"]
|
|
|
|
return results
|
|
|
|
async def run_all_tests(self, tests_dir: Path) -> list[TestResult]:
|
|
"""
|
|
Run all tests in a directory.
|
|
|
|
Args:
|
|
tests_dir: Directory containing JSON test files
|
|
|
|
Returns:
|
|
List of test results
|
|
"""
|
|
test_files = list(tests_dir.glob("*.json"))
|
|
print(f"found {len(test_files)} test files in {tests_dir}")
|
|
|
|
all_results: list[TestResult] = []
|
|
|
|
for test_file in test_files:
|
|
result = await self.execute_test(test_file)
|
|
all_results.append(result)
|
|
|
|
# Print summary for this test
|
|
print(f"\n{'=' * 60}")
|
|
print(f"Test: {result['test_name']}")
|
|
print(f"Status: {'PASS' if result['passed'] else 'FAIL'}")
|
|
print(f"Duration: {result['duration_seconds']:.2f}s")
|
|
print(f"{'=' * 60}\n")
|
|
|
|
return all_results
|
|
|
|
def print_summary(self, results: list[TestResult]) -> None:
|
|
"""
|
|
Print a summary of all test results.
|
|
|
|
Args:
|
|
results: List of test results
|
|
"""
|
|
print(f"\n{'=' * 80}")
|
|
print("TEST EXECUTION SUMMARY")
|
|
print(f"{'=' * 80}")
|
|
|
|
total_tests = len(results)
|
|
passed_tests = sum(1 for r in results if r.get("passed", False))
|
|
failed_tests = total_tests - passed_tests
|
|
total_test_time = sum(r["duration_seconds"] for r in results)
|
|
|
|
print(f"Total Tests: {total_tests}")
|
|
print(f"Passed: {passed_tests}")
|
|
print(f"Failed: {failed_tests}")
|
|
print(f"Success Rate: {(passed_tests / total_tests) * 100:.1f}%")
|
|
print(f"Total Test Time: {total_test_time:.2f}s")
|
|
|
|
print("\nDetailed Results:")
|
|
print(f"{'Test Name':<20} {'Status':<8} {'Duration':<10} {'Workspace ID':<30}")
|
|
print(f"{'-' * 20} {'-' * 8} {'-' * 10} {'-' * 30}")
|
|
|
|
for result in results:
|
|
test_name = result["test_name"]
|
|
status = "PASS" if result.get("passed", False) else "FAIL"
|
|
duration = f"{result['duration_seconds']:.2f}s"
|
|
workspace = result["workspace_id"]
|
|
|
|
print(f"{test_name:<20} {status:<8} {duration:<10} {workspace:<30}")
|
|
|
|
print(f"{'=' * 80}")
|
|
|
|
|
|
async def main() -> int:
|
|
"""
|
|
Main entry point for the test runner.
|
|
"""
|
|
parser = argparse.ArgumentParser(
|
|
description="Run JSON tests against a Honcho instance",
|
|
formatter_class=argparse.RawDescriptionHelpFormatter,
|
|
epilog="""
|
|
Examples:
|
|
%(prog)s --tests-dir tests/bench/tests # Run all tests
|
|
%(prog)s --tests-dir tests/bench/tests --test 1.json # Run specific test
|
|
%(prog)s --honcho-url http://localhost:8000 # Custom Honcho URL
|
|
""",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--tests-dir",
|
|
type=Path,
|
|
default=Path("tests/bench/tests"),
|
|
help="Directory containing JSON test files (default: tests/bench/tests)",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--test",
|
|
type=str,
|
|
help="Run a specific test file by name (e.g., '1.json') (optional)",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--honcho-url",
|
|
type=str,
|
|
default="http://localhost:8000",
|
|
help="URL of the running Honcho instance (default: http://localhost:8000)",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--anthropic-api-key",
|
|
type=str,
|
|
help="Anthropic API key for response judging (optional)",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--timeout",
|
|
type=int,
|
|
default=60,
|
|
help="Timeout for deriver queue to empty in seconds (default: 60)",
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
|
|
# Validate arguments
|
|
if args.test:
|
|
test_file_path = args.tests_dir / args.test
|
|
if not test_file_path.exists():
|
|
print(f"Error: Test file {test_file_path} does not exist")
|
|
return 1
|
|
|
|
if not args.tests_dir.exists():
|
|
print(f"Error: Tests directory {args.tests_dir} does not exist")
|
|
return 1
|
|
|
|
# Create test runner
|
|
runner = TestRunner(
|
|
honcho_url=args.honcho_url, anthropic_api_key=args.anthropic_api_key
|
|
)
|
|
|
|
try:
|
|
if args.test:
|
|
# Run single test
|
|
test_file_path = args.tests_dir / args.test
|
|
result = await runner.execute_test(test_file_path)
|
|
runner.print_summary([result])
|
|
else:
|
|
# Run all tests
|
|
results = await runner.run_all_tests(args.tests_dir)
|
|
runner.print_summary(results)
|
|
|
|
return 0
|
|
|
|
except KeyboardInterrupt:
|
|
print("\nTest execution interrupted by user")
|
|
return 1
|
|
except Exception as e:
|
|
print(f"Error running tests: {e}")
|
|
return 1
|
|
|
|
|
|
if __name__ == "__main__":
|
|
exit_code = asyncio.run(main())
|
|
exit(exit_code)
|