9db64ecda3
* chore: run benchmarks with reflect mode * chore: run benchmarks with reflect mode * fixes * new mm * bunch of fixes * initial commit * fixes * fixes * fixes * fix: sometimes memories gets extracted in the wrong language
626 lines
24 KiB
Python
626 lines
24 KiB
Python
"""
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LoComo-specific benchmark implementations.
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Provides dataset, answer generator, and evaluator for the LoComo benchmark.
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"""
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import asyncio
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import json
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import os
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import sys
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Tuple
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import pydantic
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from hindsight_api.engine.llm_wrapper import LLMConfig
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from openai import AsyncOpenAI
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from benchmarks.common.benchmark_runner import (
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BenchmarkDataset,
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BenchmarkRunner,
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LLMAnswerEvaluator,
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LLMAnswerGenerator,
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)
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class LoComoDataset(BenchmarkDataset):
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"""LoComo dataset implementation."""
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def load(self, path: Path, max_items: Optional[int] = None) -> List[Dict[str, Any]]:
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"""Load LoComo dataset from JSON file."""
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with open(path, "r") as f:
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dataset = json.load(f)
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if max_items:
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dataset = dataset[:max_items]
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return dataset
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def get_item_id(self, item: Dict) -> str:
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"""Get sample ID from LoComo item."""
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return item["sample_id"]
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def prepare_sessions_for_ingestion(self, item: Dict) -> List[Dict[str, Any]]:
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"""
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Prepare LoComo conversation for batch ingestion.
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Each session is ingested as a separate item with its own date.
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Returns:
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List of session dicts, each containing 'content', 'context', 'event_date', 'document_id'
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"""
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conv = item["conversation"]
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speaker_a = conv["speaker_a"]
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speaker_b = conv["speaker_b"]
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# Get all session keys sorted
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session_keys = sorted([k for k in conv.keys() if k.startswith("session_") and not k.endswith("_date_time")])
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session_items = []
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for session_key in session_keys:
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if session_key not in conv or not isinstance(conv[session_key], list):
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continue
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session_data = conv[session_key]
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# Get session date
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date_key = f"{session_key}_date_time"
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session_date = self._parse_date(conv.get(date_key))
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session_content = json.dumps(session_data)
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document_id = f"{item['sample_id']}_{session_key}"
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session_items.append(
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{
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"content": session_content,
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"context": f"Conversation between {speaker_a} and {speaker_b} ({session_key} of {item['sample_id']})",
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"event_date": session_date,
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"document_id": document_id,
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}
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)
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return session_items
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def get_qa_pairs(self, item: Dict) -> List[Dict[str, Any]]:
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"""
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Extract QA pairs from LoComo item.
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Returns:
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List of QA dicts with 'question', 'answer', 'category'
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"""
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return item["qa"]
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def _parse_date(self, date_string: str) -> datetime:
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"""Parse LoComo date format to datetime."""
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# Format: "1:56 pm on 8 May, 2023"
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try:
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dt = datetime.strptime(date_string, "%I:%M %p on %d %B, %Y")
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return dt.replace(tzinfo=timezone.utc)
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except:
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raise
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class QuestionAnswer(pydantic.BaseModel):
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"""Answer format for LoComo questions."""
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answer: str
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reasoning: str
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class LoComoAnswerGenerator(LLMAnswerGenerator):
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"""LoComo-specific answer generator using configurable LLM provider."""
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def __init__(self):
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"""Initialize with LLM configuration for answer generation."""
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self.llm_config = LLMConfig.for_answer_generation()
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self.client = self.llm_config._client
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self.model = self.llm_config.model
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async def generate_answer(
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self,
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question: str,
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recall_result: Dict[str, Any],
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question_date: Optional[datetime] = None,
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question_type: Optional[str] = None,
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bank_id: Optional[str] = None,
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) -> Tuple[str, str, Optional[List[Dict[str, Any]]]]:
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"""
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Generate answer from retrieved memories using Groq.
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Args:
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question: The question text
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recall_result: Full RecallResult dict containing results, entities, chunks, and trace
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question_date: Date when the question was asked (for temporal context)
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question_type: Question category (unused in Locomo)
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Returns:
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Tuple of (answer, reasoning, None)
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- None indicates to use the memories from recall_result
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"""
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context = json.dumps(recall_result)
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# Format question date if provided
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question_date_str = ""
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if question_date:
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question_date_str = f"\n# CURRENT DATE:\nThe question is being asked on: {question_date.strftime('%Y-%m-%d %H:%M:%S')} UTC\n"
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# Use LLM to generate answer
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try:
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answer_obj = await self.llm_config.call(
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messages=[
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{
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"role": "system",
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"content": "You are a helpful expert assistant answering questions from lme_experiment users based on the provided context.",
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},
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{
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"role": "user",
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"content": f"""
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# CONTEXT:
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You have access to facts and entities from a conversation.
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{question_date_str}
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# INSTRUCTIONS:
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1. Carefully analyze all provided memories
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2. Pay special attention to the timestamps to determine the answer
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3. If the question asks about a specific event or fact, look for direct evidence in the memories
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4. If the memories contain contradictory information or multiple instances of an event, say them all
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5. Always convert relative time references to specific dates, months, or years.
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6. Be as specific as possible when talking about people, places, and events
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7. If the answer is not explicitly stated in the memories, use logical reasoning based on the information available to answer (e.g. calculate duration of an event from different memories).
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Context:
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{context}
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Question: {question}
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Answer:
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""",
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},
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],
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response_format=QuestionAnswer,
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scope="memory",
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)
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return answer_obj.answer, answer_obj.reasoning, None
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except Exception as e:
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return f"Error generating answer: {str(e)}", "Error occurred during answer generation.", None
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class LoComoThinkAnswerGenerator(LLMAnswerGenerator):
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"""LoComo answer generator using the think API instead of search + LLM.
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This generator performs its own retrieval internally via the think API,
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so it doesn't need external search to be performed by the benchmark runner.
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"""
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def __init__(self, memory: "MemoryEngine", agent_id: str, thinking_budget: int = 500):
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"""Initialize with memory instance and agent_id.
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Args:
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memory: MemoryEngine instance
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agent_id: Agent identifier for think queries
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thinking_budget: Budget for memory exploration
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"""
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self.memory = memory
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self.agent_id = agent_id
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self.thinking_budget = thinking_budget
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def needs_external_search(self) -> bool:
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"""Think API does its own retrieval, so no external search needed."""
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return False
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async def generate_answer(
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self,
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question: str,
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recall_result: Dict[str, Any],
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question_date: Optional[datetime] = None,
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question_type: Optional[str] = None,
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bank_id: Optional[str] = None,
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) -> Tuple[str, str, Optional[List[Dict[str, Any]]]]:
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"""
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Generate answer using the integrated think API.
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The think API performs both search and answer generation in a single call,
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combining agent facts, world facts, and opinions to formulate a response.
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Args:
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question: Question to answer
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recall_result: Not used (empty dict), as think does its own retrieval
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question_date: Date when the question was asked (currently not used by think API)
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question_type: Question category (unused in think API)
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bank_id: Not used - think API uses self.agent_id from constructor
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Returns:
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Tuple of (answer, reasoning, retrieved_memories)
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- retrieved_memories: Combined list of all facts from based_on (world, agent, opinion)
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"""
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try:
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# Use the think API which does both search and answer generation
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result = await self.memory.think_async(
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agent_id=self.agent_id,
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query=question,
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thinking_budget=self.thinking_budget,
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)
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# Extract answer and reasoning
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answer = result.text
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# Extract memories from based_on
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based_on = result.based_on
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world_facts = based_on.get("world", [])
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agent_facts = based_on.get("agent", [])
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opinion_facts = based_on.get("opinion", [])
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# Combine all facts into retrieved_memories
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retrieved_memories = []
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# Add world facts
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for fact in world_facts:
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retrieved_memories.append(fact.model_dump())
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for fact in agent_facts:
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retrieved_memories.append(fact.model_dump())
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for fact in opinion_facts:
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retrieved_memories.append(fact.model_dump())
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# Build reasoning summary
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num_world = len(world_facts)
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num_agent = len(agent_facts)
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num_opinion = len(opinion_facts)
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reasoning = f"Think API: {num_world} world facts, {num_agent} agent facts, {num_opinion} opinions"
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return answer, reasoning, retrieved_memories
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except Exception as e:
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return f"Error generating answer: {str(e)}", "Error occurred during think API call.", []
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async def run_benchmark(
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max_conversations: int = None,
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max_questions_per_conv: int = None,
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skip_ingestion: bool = False,
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use_think: bool = False,
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include_mental_models: bool = False,
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only_mental_models: bool = False,
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conversation: str = None,
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api_url: str = None,
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max_concurrent_questions_override: int = None,
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only_failed: bool = False,
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only_invalid: bool = False,
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):
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"""
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Run the LoComo benchmark.
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Args:
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max_conversations: Maximum number of conversations to evaluate (None for all)
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max_questions_per_conv: Maximum questions per conversation (None for all)
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skip_ingestion: Whether to skip ingestion and use existing data
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use_think: Whether to use the think API instead of search + LLM
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include_mental_models: If True, include mental models in recall results and wait for consolidation after ingestion.
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only_mental_models: If True, only retrieve mental models (no facts). Implies waiting for consolidation.
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conversation: Specific conversation ID to run (e.g., "conv-26")
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api_url: Optional API URL to connect to (default: use local memory)
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only_failed: If True, only run conversations that have failed questions (is_correct=False)
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only_invalid: If True, only run conversations that have invalid questions (is_invalid=True)
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"""
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from rich.console import Console
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console = Console()
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# Load previous results if filtering for failed/invalid conversations
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failed_conversation_ids = set()
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invalid_conversation_ids = set()
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if only_failed or only_invalid:
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suffix = "_think" if use_think else ""
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results_filename = f"benchmark_results{suffix}.json"
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results_path = Path(__file__).parent / "results" / results_filename
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if not results_path.exists():
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console.print("[red]Error: Cannot use --only-failed or --only-invalid without existing results file[/red]")
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console.print(f"[yellow]Results file not found: {results_path}[/yellow]")
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return
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with open(results_path, "r") as f:
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previous_results = json.load(f)
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# Extract conversation IDs that have failed or invalid questions
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for item_result in previous_results.get("item_results", []):
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item_id = item_result["item_id"]
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for detail in item_result["metrics"].get("detailed_results", []):
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if only_failed and detail.get("is_correct") == False and not detail.get("is_invalid", False):
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failed_conversation_ids.add(item_id)
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if only_invalid and detail.get("is_invalid", False):
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invalid_conversation_ids.add(item_id)
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if only_failed:
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console.print(
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f"[cyan]Filtering to {len(failed_conversation_ids)} conversations with failed questions (is_correct=False)[/cyan]"
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)
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if only_invalid:
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console.print(
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f"[cyan]Filtering to {len(invalid_conversation_ids)} conversations with invalid questions (is_invalid=True)[/cyan]"
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)
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target_ids = failed_conversation_ids if only_failed else invalid_conversation_ids
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if not target_ids:
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filter_type = "failed" if only_failed else "invalid"
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console.print(
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f"[yellow]No conversations with {filter_type} questions found in previous results. Nothing to run.[/yellow]"
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)
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return
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# Initialize components
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dataset = LoComoDataset()
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# Use remote API client if api_url is provided, otherwise use local memory
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if api_url:
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from benchmarks.common.benchmark_runner import HindsightClientAdapter
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memory = HindsightClientAdapter(base_url=api_url)
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await memory.initialize()
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else:
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from benchmarks.common.benchmark_runner import create_memory_engine
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memory = await create_memory_engine()
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# Select answer generator based on mode
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from hindsight_api.engine.memory_engine import Budget
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if use_think:
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console.print("[blue]Mode: think (using think API)[/blue]")
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answer_generator = LoComoThinkAnswerGenerator(memory=memory, agent_id="locomo", thinking_budget=500)
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max_concurrent_questions = max_concurrent_questions_override or 4
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eval_semaphore_size = 4
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else:
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console.print("[blue]Mode: recall+LLM (traditional)[/blue]")
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answer_generator = LoComoAnswerGenerator()
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# Reduced from 32 to 10 to match search semaphore limit
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# Prevents "too many connections" errors
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max_concurrent_questions = max_concurrent_questions_override or 10
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eval_semaphore_size = 8
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answer_evaluator = LLMAnswerEvaluator()
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# Create benchmark runner
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runner = BenchmarkRunner(
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dataset=dataset, answer_generator=answer_generator, answer_evaluator=answer_evaluator, memory=memory
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)
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# Filter dataset if using --only-failed or --only-invalid
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dataset_path = Path(__file__).parent / "datasets" / "locomo10.json"
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if only_failed or only_invalid:
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# Load and filter dataset
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target_ids = failed_conversation_ids if only_failed else invalid_conversation_ids
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original_items = dataset.load(dataset_path, max_conversations)
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filtered_items = [item for item in original_items if dataset.get_item_id(item) in target_ids]
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console.print(f"[green]Found {len(filtered_items)} conversations to re-evaluate[/green]")
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# Temporarily replace dataset's load method
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original_load = dataset.load
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def filtered_load(path: Path, max_items: Optional[int] = None):
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return filtered_items[:max_items] if max_items else filtered_items
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dataset.load = filtered_load
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# Determine output filename based on mode
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if use_think:
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suffix = "_think"
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elif only_mental_models:
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suffix = "_only_mental_models"
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elif include_mental_models:
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suffix = "_mental_models"
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else:
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suffix = ""
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results_filename = f"benchmark_results{suffix}.json"
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output_path = Path(__file__).parent / "results" / results_filename
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# Create results directory if it doesn't exist
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output_path.parent.mkdir(parents=True, exist_ok=True)
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# Merge with existing results if running a specific conversation or using filters
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merge_with_existing = conversation is not None or only_failed or only_invalid
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# Each conversation gets its own isolated bank
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separate_ingestion = False
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clear_per_item = True # Use unique agent ID per conversation
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if include_mental_models or only_mental_models:
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# Mental models requires more time due to consolidation, limit parallelism
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concurrent_items = 2
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else:
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concurrent_items = 3 # Process up to 3 conversations in parallel
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# Run benchmark with parallel conversation processing
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# Each conversation gets its own agent ID (locomo_conv-26, locomo_conv-30, etc.)
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# This allows conversations to run in parallel (up to max_concurrent_items at a time)
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results = await runner.run(
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dataset_path=dataset_path,
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agent_id="locomo",
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max_items=max_conversations,
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max_questions_per_item=max_questions_per_conv,
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thinking_budget=500,
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max_tokens=4096,
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skip_ingestion=skip_ingestion,
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max_concurrent_questions=max_concurrent_questions,
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eval_semaphore_size=eval_semaphore_size,
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specific_item=conversation,
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separate_ingestion_phase=separate_ingestion,
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clear_agent_per_item=clear_per_item,
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max_concurrent_items=concurrent_items,
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output_path=output_path, # Save results incrementally
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merge_with_existing=merge_with_existing,
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include_mental_models=include_mental_models, # Include mental models in recall results
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only_mental_models=only_mental_models, # Only retrieve mental models (no facts)
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)
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# Display results (final save already happened incrementally)
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runner.display_results(results)
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console.print(f"\n[green]✓[/green] Results saved incrementally to {output_path}")
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# Generate markdown table
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generate_markdown_table(
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results, use_think=use_think, include_mental_models=include_mental_models, only_mental_models=only_mental_models
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)
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return results
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def generate_markdown_table(
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results: dict, use_think: bool = False, include_mental_models: bool = False, only_mental_models: bool = False
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):
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"""
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Generate a markdown table with benchmark results.
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Category mapping:
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1 = Multi-hop
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2 = Single-hop
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3 = Temporal
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4 = Open-domain
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"""
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from rich.console import Console
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console = Console()
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category_names = {"1": "Multi-hop", "2": "Single-hop", "3": "Temporal", "4": "Open-domain"}
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# Build markdown content
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lines = []
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if use_think:
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mode_str = " (Think Mode)"
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elif only_mental_models:
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mode_str = " (Only Mental Models Mode)"
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elif include_mental_models:
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mode_str = " (Mental Models Mode)"
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else:
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mode_str = ""
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lines.append(f"# LoComo Benchmark Results{mode_str}")
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lines.append("")
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# Add model configuration
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if "model_config" in results:
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config = results["model_config"]
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lines.append("## Model Configuration")
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lines.append("")
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lines.append(f"- **Hindsight**: {config['hindsight']['provider']}/{config['hindsight']['model']}")
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lines.append(
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f"- **Answer Generation**: {config['answer_generation']['provider']}/{config['answer_generation']['model']}"
|
|
)
|
|
lines.append(f"- **LLM Judge**: {config['judge']['provider']}/{config['judge']['model']}")
|
|
lines.append("")
|
|
|
|
lines.append(
|
|
f"**Overall Accuracy**: {results['overall_accuracy']:.2f}% ({results['total_correct']}/{results['total_questions']})"
|
|
)
|
|
lines.append("")
|
|
lines.append(
|
|
"| Sample ID | Sessions | Questions | Correct | Accuracy | Multi-hop | Single-hop | Temporal | Open-domain |"
|
|
)
|
|
lines.append(
|
|
"|-----------|----------|-----------|---------|----------|-----------|------------|----------|-------------|"
|
|
)
|
|
|
|
for item_result in results["item_results"]:
|
|
item_id = item_result["item_id"]
|
|
num_sessions = item_result["num_sessions"]
|
|
metrics = item_result["metrics"]
|
|
|
|
# Calculate category accuracies
|
|
cat_stats = metrics.get("category_stats", {})
|
|
cat_accuracies = {}
|
|
|
|
for cat_id in ["1", "2", "3", "4"]:
|
|
if cat_id in cat_stats:
|
|
stats = cat_stats[cat_id]
|
|
acc = (stats["correct"] / stats["total"] * 100) if stats["total"] > 0 else 0
|
|
cat_accuracies[cat_id] = f"{acc:.1f}% ({stats['correct']}/{stats['total']})"
|
|
else:
|
|
cat_accuracies[cat_id] = "N/A"
|
|
|
|
lines.append(
|
|
f"| {item_id} | {num_sessions} | {metrics['total']} | {metrics['correct']} | "
|
|
f"{metrics['accuracy']:.2f}% | {cat_accuracies['1']} | {cat_accuracies['2']} | "
|
|
f"{cat_accuracies['3']} | {cat_accuracies['4']} |"
|
|
)
|
|
|
|
# Write to file with suffix
|
|
if use_think:
|
|
suffix = "_think"
|
|
elif only_mental_models:
|
|
suffix = "_only_mental_models"
|
|
elif include_mental_models:
|
|
suffix = "_mental_models"
|
|
else:
|
|
suffix = ""
|
|
output_file = Path(__file__).parent / "results" / f"results_table{suffix}.md"
|
|
output_file.parent.mkdir(parents=True, exist_ok=True)
|
|
output_file.write_text("\n".join(lines))
|
|
console.print(f"\n[green]✓[/green] Results table saved to {output_file}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import argparse
|
|
import logging
|
|
|
|
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
|
|
|
|
parser = argparse.ArgumentParser(description="Run LoComo benchmark")
|
|
parser.add_argument("--max-conversations", type=int, default=None, help="Maximum conversations to evaluate")
|
|
parser.add_argument("--max-questions", type=int, default=None, help="Maximum questions per conversation")
|
|
parser.add_argument("--skip-ingestion", action="store_true", help="Skip ingestion and use existing data")
|
|
parser.add_argument("--use-think", action="store_true", help="Use think API instead of search + LLM")
|
|
parser.add_argument(
|
|
"--conversation", type=str, default=None, help='Run only specific conversation (e.g., "conv-26")'
|
|
)
|
|
parser.add_argument(
|
|
"--api-url",
|
|
type=str,
|
|
default=None,
|
|
help="Hindsight API URL (default: use local memory, example: http://localhost:8888)",
|
|
)
|
|
parser.add_argument(
|
|
"--max-concurrent-questions",
|
|
type=int,
|
|
default=None,
|
|
help="Max concurrent questions per conversation (default: 4 for think, 10 for search)",
|
|
)
|
|
parser.add_argument(
|
|
"--only-failed",
|
|
action="store_true",
|
|
help="Only run conversations that have failed questions (is_correct=False). Requires existing results file.",
|
|
)
|
|
parser.add_argument(
|
|
"--only-invalid",
|
|
action="store_true",
|
|
help="Only run conversations that have invalid questions (is_invalid=True). Requires existing results file.",
|
|
)
|
|
parser.add_argument(
|
|
"--include-mental-models",
|
|
action="store_true",
|
|
help="Include mental models in recall results. This waits for consolidation to complete after ingestion and includes mental models in the recall response.",
|
|
)
|
|
parser.add_argument(
|
|
"--only-mental-models",
|
|
action="store_true",
|
|
help="Only retrieve mental models (no facts). This waits for consolidation to complete after ingestion and only returns mental models.",
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
|
|
# Validate that only one of --only-failed or --only-invalid is set
|
|
if args.only_failed and args.only_invalid:
|
|
parser.error("Cannot use both --only-failed and --only-invalid at the same time")
|
|
|
|
results = asyncio.run(
|
|
run_benchmark(
|
|
max_conversations=args.max_conversations,
|
|
max_questions_per_conv=args.max_questions,
|
|
skip_ingestion=args.skip_ingestion,
|
|
use_think=args.use_think,
|
|
include_mental_models=args.include_mental_models,
|
|
only_mental_models=args.only_mental_models,
|
|
conversation=args.conversation,
|
|
api_url=args.api_url,
|
|
max_concurrent_questions_override=args.max_concurrent_questions,
|
|
only_failed=args.only_failed,
|
|
only_invalid=args.only_invalid,
|
|
)
|
|
)
|