chopratejas--headroom
0ef5fcb1c5
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93 行
2.1 KiB
Python
93 行
2.1 KiB
Python
"""Memory evaluation framework for Headroom.
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Benchmarks for evaluating memory system quality using industry-standard
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datasets like LoCoMo.
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Evaluates:
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- Memory extraction quality
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- Semantic retrieval accuracy
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- End-to-end QA performance
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- Hierarchical scoping
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- Temporal versioning
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Example:
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from headroom.evals.memory import LoCoMoEvaluator, MemoryEvalConfig
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async def my_answer_fn(question: str, memories: list[str]) -> str:
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# Your LLM-based answerer here
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return "..."
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evaluator = LoCoMoEvaluator(
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answer_fn=my_answer_fn,
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config=MemoryEvalConfig(n_conversations=5),
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)
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result = await evaluator.run()
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print(result.summary())
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"""
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from headroom.evals.memory.judge import (
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create_anthropic_judge,
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create_litellm_judge,
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create_openai_judge,
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simple_judge,
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)
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from headroom.evals.memory.locomo import (
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CATEGORY_DESCRIPTIONS,
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LOCOMO_CATEGORIES,
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LoCoMoCase,
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LoCoMoConversation,
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LoCoMoResult,
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get_locomo_stats,
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load_locomo,
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)
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from headroom.evals.memory.runner import (
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LoCoMoEvaluator,
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MemoryEvalConfig,
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MemoryEvalResult,
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MemoryEvalSuiteResult,
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run_locomo_eval,
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run_locomo_eval_sync,
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)
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from headroom.evals.memory.runner_v2 import (
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EvalMetrics,
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LoCoMoEvaluatorV2,
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MemoryEvalConfigV2,
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MemoryEvalResultV2,
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MemoryEvalSuiteResultV2,
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run_locomo_eval_v2,
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run_locomo_eval_v2_sync,
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)
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__all__ = [
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# Dataset loading
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"load_locomo",
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"get_locomo_stats",
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# Data models
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"LoCoMoConversation",
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"LoCoMoCase",
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"LoCoMoResult",
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# Constants
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"LOCOMO_CATEGORIES",
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"CATEGORY_DESCRIPTIONS",
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# V1 Evaluation (explicit extraction)
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"LoCoMoEvaluator",
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"MemoryEvalConfig",
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"MemoryEvalResult",
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"MemoryEvalSuiteResult",
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"run_locomo_eval",
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"run_locomo_eval_sync",
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# V2 Evaluation (LLM-controlled tools)
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"LoCoMoEvaluatorV2",
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"MemoryEvalConfigV2",
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"MemoryEvalResultV2",
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"MemoryEvalSuiteResultV2",
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"EvalMetrics",
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"run_locomo_eval_v2",
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"run_locomo_eval_v2_sync",
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# LLM Judge
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"create_openai_judge",
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"create_anthropic_judge",
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"create_litellm_judge",
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"simple_judge",
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]
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