diff --git a/.gitignore b/.gitignore index 4dbac8c29..eb2b2dbcd 100644 --- a/.gitignore +++ b/.gitignore @@ -184,3 +184,4 @@ wandb **/api_keys.json weights.csv past_websites.csv +timer_logs* diff --git a/neurons/miners/epistula_miner/miner.py b/neurons/miners/epistula_miner/miner.py index 573dc30ea..aca2c6e45 100644 --- a/neurons/miners/epistula_miner/miner.py +++ b/neurons/miners/epistula_miner/miner.py @@ -220,7 +220,7 @@ def run(self): async def run_inference(self, request: Request) -> str: data = await request.json() try: - response = self.llm.generate( + response = await self.llm.generate( data.get("messages"), sampling_params=data.get("sampling_parameters"), seed=data.get("seed") ) return response diff --git a/neurons/validator.py b/neurons/validator.py index 771ad8c24..de9a5d44f 100644 --- a/neurons/validator.py +++ b/neurons/validator.py @@ -1,11 +1,13 @@ import asyncio -import multiprocessing as mp import sys import loguru import netaddr import requests import torch + +# import multiprocessing as mp +import torch.multiprocessing as mp import wandb from bittensor.core.extrinsics.serving import serve_extrinsic @@ -29,40 +31,29 @@ NEURON_SAMPLE_SIZE = 100 # TODO: Should add this to constants.py -def create_loop_process(task_queue, scoring_queue, reward_events): +def create_loop_process(task_queue, scoring_queue, reward_events, miners_dict): settings.shared_settings = settings.SharedSettings.load(mode="validator") if settings.shared_settings.WANDB_ON: init_wandb(neuron="validator") - async def spawn_loops(task_queue, scoring_queue, reward_events): + async def spawn_loops(task_queue, scoring_queue, reward_events, miners_dict): # ruff: noqa: E402 from prompting.llms.model_manager import model_scheduler - from prompting.miner_availability.miner_availability import availability_checking_loop + + # from prompting.miner_availability.miner_availability import availability_checking_loop from prompting.tasks.task_creation import task_loop - from prompting.tasks.task_sending import task_sender - from prompting.weight_setting.weight_setter import weight_setter from shared.profiling import profiler logger.info("Starting Profiler...") asyncio.create_task(profiler.print_stats(), name="Profiler"), - # -------- Duplicate of create_task_loop ---------- - logger.info("Starting AvailabilityCheckingLoop...") - asyncio.create_task(availability_checking_loop.start()) - - logger.info("Starting TaskSender...") - asyncio.create_task(task_sender.start(task_queue, scoring_queue)) - logger.info("Starting TaskLoop...") - asyncio.create_task(task_loop.start(task_queue, scoring_queue)) - # ------------------------------------------------- + asyncio.create_task(task_loop.start(task_queue, scoring_queue, miners_dict, simultaneous_loops=4)) logger.info("Starting ModelScheduler...") asyncio.create_task(model_scheduler.start(scoring_queue), name="ModelScheduler"), logger.info("Starting TaskScorer...") - asyncio.create_task(task_scorer.start(scoring_queue, reward_events), name="TaskScorer"), - logger.info("Starting WeightSetter...") - asyncio.create_task(weight_setter.start(reward_events)) + asyncio.create_task(task_scorer.start(scoring_queue, reward_events, simultaneous_loops=4), name="TaskScorer"), while True: await asyncio.sleep(5) @@ -73,9 +64,9 @@ async def spawn_loops(task_queue, scoring_queue, reward_events): logger.debug(f"Number of tasks in Reward Events: {len(reward_events)}") try: - asyncio.run(spawn_loops(task_queue, scoring_queue, reward_events)) + asyncio.run(spawn_loops(task_queue, scoring_queue, reward_events, miners_dict)) except Exception as e: - logger.info(f"Terminating loop process: {e}") + logger.exception(f"Terminating loop process: {e}") finally: logger.info("Cleaning up resources...") @@ -85,16 +76,10 @@ async def spawn_loops(task_queue, scoring_queue, reward_events): logger.info("WandB run finished.") -def start_api(scoring_queue, reward_events): +def start_api(scoring_queue, reward_events, miners_dict): async def start(): from prompting.api.api import start_scoring_api # noqa: F401 - # TODO: We should not use 2 availability loops for each process, in reality - # we should only be sharing the miner availability data between processes. - from prompting.miner_availability.miner_availability import availability_checking_loop - - asyncio.create_task(availability_checking_loop.start()) - try: external_ip = requests.get("https://checkip.amazonaws.com").text.strip() netaddr.IPAddress(external_ip) @@ -111,7 +96,7 @@ async def start(): logger.debug(f"Serve success: {serve_success}") except Exception as e: logger.warning(f"Failed to serve scoring api to chain: {e}") - await start_scoring_api(task_scorer, scoring_queue, reward_events) + await start_scoring_api(task_scorer, scoring_queue, reward_events, miners_dict) while True: await asyncio.sleep(10) @@ -119,29 +104,113 @@ async def start(): asyncio.run(start()) +def start_task_sending_loop(task_queue, scoring_queue, miners_dict: dict): + async def spawn_loops(task_queue, scoring_queue, miners_dict: dict): + from prompting.tasks.task_sending import task_sender + + logger.info("Starting task sending loop in validator2...") + asyncio.create_task(task_sender.start(task_queue, scoring_queue, miners_dict, simultaneous_loops=10)) + while True: + await asyncio.sleep(5) + logger.debug("Task sending loop is running") + + try: + logger.info("Starting task sending loop in validator...") + asyncio.run(spawn_loops(task_queue, scoring_queue, miners_dict)) + + except Exception as e: + logger.exception(f"Task sending loop error: {e}") + raise + + +def start_availability_checking_loop(miners_dict: dict): + async def spawn_loops(miners_dict: dict): + from prompting.miner_availability.miner_availability import availability_checking_loop + + logger.info("Starting availability checking loop in validator2...") + asyncio.create_task(availability_checking_loop.start(miners_dict)) + while True: + await asyncio.sleep(5) + logger.debug("Availability checking loop is running") + + try: + logger.info("Starting availability checking loop in validator...") + asyncio.run(spawn_loops(miners_dict)) + + except Exception as e: + logger.exception(f"Availability checking loop error: {e}") + raise + + +def start_weight_setter_loop(reward_events): + async def spawn_loops(reward_events): + from prompting.weight_setting.weight_setter import weight_setter + + logger.info("Starting weight setter loop in validator2...") + asyncio.create_task(weight_setter.start(reward_events)) + while True: + await asyncio.sleep(5) + logger.debug("Weight setter loop is running") + + try: + logger.info("Starting weight setter loop in validator...") + asyncio.run(spawn_loops(reward_events)) + + except Exception as e: + logger.exception(f"Weight setter loop error: {e}") + raise + + async def main(): # will start checking the availability of miners at regular intervals, needed for API and Validator with torch.multiprocessing.Manager() as manager: reward_events = manager.list() scoring_queue = manager.list() task_queue = manager.list() - - # Create process pool for managed processes + miners_dict = manager.dict() processes = [] try: - # # Start checking the availability of miners at regular intervals + # Start checking the availability of miners at regular intervals if settings.shared_settings.DEPLOY_SCORING_API: # Use multiprocessing to bypass API blocking issue - api_process = mp.Process(target=start_api, args=(scoring_queue, reward_events), name="API_Process") + api_process = mp.Process( + target=start_api, args=(scoring_queue, reward_events, miners_dict), name="API_Process" + ) api_process.start() processes.append(api_process) - loop_process = mp.Process( - target=create_loop_process, args=(task_queue, scoring_queue, reward_events), name="LoopProcess" + availability_process = mp.Process( + target=start_availability_checking_loop, + args=(miners_dict,), + name="AvailabilityProcess", ) + availability_process.start() + processes.append(availability_process) + loop_process = mp.Process( + target=create_loop_process, + args=(task_queue, scoring_queue, reward_events, miners_dict), + name="LoopProcess", + ) loop_process.start() + + task_sending_process = mp.Process( + target=start_task_sending_loop, + args=(task_queue, scoring_queue, miners_dict), + name="TaskSendingProcess", + ) + task_sending_process.start() + processes.append(task_sending_process) + + weight_setter_process = mp.Process( + target=start_weight_setter_loop, + args=(reward_events,), + name="WeightSetterProcess", + ) + weight_setter_process.start() + processes.append(weight_setter_process) + processes.append(loop_process) GPUInfo.log_gpu_info() diff --git a/poetry.lock b/poetry.lock index 58bd24e61..901f225e6 100644 --- a/poetry.lock +++ b/poetry.lock @@ -262,13 +262,13 @@ files = [ [[package]] name = "async-substrate-interface" -version = "1.0.7" +version = "1.0.8" description = "Asyncio library for interacting with substrate. 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b/prompting/api/miner_availabilities/api.py index 75a737c56..bceea81df 100644 --- a/prompting/api/miner_availabilities/api.py +++ b/prompting/api/miner_availabilities/api.py @@ -1,18 +1,18 @@ from typing import Literal -from fastapi import APIRouter +from fastapi import APIRouter, Request -from prompting.miner_availability.miner_availability import miner_availabilities +from prompting.miner_availability.miner_availability import MinerAvailabilities from prompting.tasks.task_registry import TaskRegistry router = APIRouter() @router.post("/miner_availabilities") -async def get_miner_availabilities(uids: list[int] | None = None): +async def get_miner_availabilities(request: Request, uids: list[int] | None = None): if uids: - return {uid: miner_availabilities.miners.get(uid) for uid in uids} - return miner_availabilities.miners + return {uid: request.app.state.miners_dict.get(uid) for uid in uids} + return request.app.state.miners_dict @router.get("/get_available_miners") @@ -23,4 +23,4 @@ async def get_available_miners( ): task_configs = [config for config in TaskRegistry.task_configs if config.task.__name__ == task] task_config = task_configs[0] if task_configs else None - return miner_availabilities.get_available_miners(task=task_config, model=model, k=k) + return MinerAvailabilities.get_available_miners(task=task_config.task, model=model, k=k) diff --git a/prompting/llms/hf_llm.py b/prompting/llms/hf_llm.py index 540bee856..b8977d776 100644 --- a/prompting/llms/hf_llm.py +++ b/prompting/llms/hf_llm.py @@ -1,5 +1,7 @@ +import asyncio import random from abc import abstractmethod +from functools import partial import numpy as np from loguru import logger @@ -15,13 +17,14 @@ def __init__(self, model_id: str, device: str, sampling_params: dict[str, str | self.model_id = model_id self._device = device self.sampling_params = sampling_params if sampling_params else {} + self.message_formatter = ReproducibleHF.format_messages @staticmethod @abstractmethod def format_messages(messages: list[str] | list[dict[str, str]]) -> list[dict[str, str | list[dict[str, str]]]]: raise NotImplementedError("This method must be implemented by the subclass") - def generate( + async def generate( self, messages: list[str] | list[dict[str, str]], sampling_params: dict[str, str | float | int | bool] | None = None, @@ -30,27 +33,43 @@ def generate( """Generate text with optimized performance.""" with torch.inference_mode(): self.set_random_seeds(seed) - - inputs = self.tokenizer.apply_chat_template( - self.message_formater(messages), - tokenize=True, - add_generation_prompt=True, - return_tensors="pt", - return_dict=True, - ).to(self._device) + # Move tokenization to a background thread since it can be CPU intensive + loop = asyncio.get_event_loop() + inputs = await loop.run_in_executor( + None, + partial( + self.tokenizer.apply_chat_template, + self.message_formatter(messages), + tokenize=True, + add_generation_prompt=True, + return_tensors="pt", + return_dict=True, + ), + ) + inputs = inputs.to(self._device) params = sampling_params if sampling_params else self.sampling_params filtered_params = {k: v for k, v in params.items() if k in self.valid_generation_params} - outputs = self.model.generate( - **inputs, - **filtered_params, + # Run model generation in a background thread to avoid blocking + outputs = await loop.run_in_executor( + None, + partial( + self.model.generate, + **inputs, + **filtered_params, + ), ) - results = self.tokenizer.batch_decode( - outputs[:, inputs["input_ids"].shape[1] :], - skip_special_tokens=True, - )[0] + # Decode outputs in background thread + results = await loop.run_in_executor( + None, + partial( + self.tokenizer.batch_decode, + outputs[:, inputs["input_ids"].shape[1] :], + skip_special_tokens=True, + ), + ) return results if len(results) > 1 else results[0] diff --git a/prompting/llms/hf_text.py b/prompting/llms/hf_text.py index fcce7178f..9453e218c 100644 --- a/prompting/llms/hf_text.py +++ b/prompting/llms/hf_text.py @@ -25,7 +25,7 @@ def __init__( ) self.tokenizer = AutoTokenizer.from_pretrained(model_id) self.valid_generation_params = set(self.model.generation_config.to_dict().keys()) - self.message_formater = self.format_messages + self.message_formatter = self.format_messages @staticmethod def format_messages(messages: list[str] | list[dict[str, str]]) -> list[dict[str, str | list[dict[str, str]]]]: diff --git a/prompting/llms/hf_text_image.py b/prompting/llms/hf_text_image.py index 2b7709e71..3e5b5565e 100644 --- a/prompting/llms/hf_text_image.py +++ b/prompting/llms/hf_text_image.py @@ -24,7 +24,7 @@ def __init__( ) self.tokenizer = AutoProcessor.from_pretrained(model_id) self.valid_generation_params = set(self.model.generation_config.to_dict().keys()) - self.message_formater = HFTextImageToText.format_messages + self.message_formatter = HFTextImageToText.format_messages @staticmethod def format_messages(messages: list[str] | list[dict[str, str]]) -> list[dict[str, str | list[dict[str, str]]]]: diff --git a/prompting/llms/model_manager.py b/prompting/llms/model_manager.py index a12178ff8..f93556523 100644 --- a/prompting/llms/model_manager.py +++ b/prompting/llms/model_manager.py @@ -231,7 +231,7 @@ def _make_prompt(self, messages: list[dict[str, str]]) -> str: composed_prompt.append(role_template["end"]) return "".join(composed_prompt) - def generate( + async def generate( self, messages: list[str], roles: list[str] | None = None, @@ -250,7 +250,7 @@ def generate( model = ModelZoo.get_random(max_ram=self.total_ram) model_instance: ReproducibleHF = self.get_model(model) - responses = model_instance.generate(messages=[dict_messages], sampling_params=sampling_params, seed=seed) + responses = await model_instance.generate(messages=[dict_messages], sampling_params=sampling_params, seed=seed) return responses @@ -301,9 +301,9 @@ class AsyncModelScheduler(AsyncLoopRunner): interval: int = 14400 scoring_queue: list | None = None - async def start(self, scoring_queue: list, name: str | None = None): + async def start(self, scoring_queue: list, name: str | None = None, **kwargs): self.scoring_queue = scoring_queue - return await super().start(name=name) + return await super().start(name=name, **kwargs) async def initialise_loop(self): model_manager.load_always_active_models() diff --git a/prompting/miner_availability/miner_availability.py b/prompting/miner_availability/miner_availability.py index fd73e7497..62e2442a5 100644 --- a/prompting/miner_availability/miner_availability.py +++ b/prompting/miner_availability/miner_availability.py @@ -4,7 +4,6 @@ import numpy as np from loguru import logger -from pydantic import BaseModel from prompting.llms.model_zoo import ModelZoo from prompting.tasks.base_task import BaseTask @@ -20,35 +19,19 @@ model_config: dict[str, bool] = {conf.llm_model_id: False for conf in ModelZoo.models_configs} -class MinerAvailability(BaseModel): - """This class keeps track of one miner's availability""" - - task_availabilities: dict[str, bool] = task_config - llm_model_availabilities: dict[str, bool] = model_config - - def is_model_available(self, model: str) -> bool: - return self.llm_model_availabilities.get(model, False) - - def is_task_available(self, task: BaseTask | type[BaseTask]) -> bool: - if isinstance(task, BaseTask): - return self.task_availabilities.get(task.__class__.__name__, False) - return self.task_availabilities.get(task.__name__, False) - - -class MinerAvailabilities(BaseModel): - """This class keeps track of all the miner's availabilities and - let's us target a miner based on its availability""" - - miners: dict[int, MinerAvailability] = {} +class MinerAvailabilities: + """Static class that provides methods to query miner availabilities from a miners dictionary""" + @staticmethod def get_available_miners( - self, task: BaseTask | None = None, model: str | None = None, k: int | None = None + miners: dict[int, dict], task: BaseTask | None = None, model: str | None = None, k: int | None = None ) -> list[int]: - available = list(self.miners.keys()) + available = list(miners.keys()) if task: - available = [uid for uid in available if self.miners[uid].is_task_available(task)] + task_name = task.__class__.__name__ if isinstance(task, BaseTask) else task.__name__ + available = [uid for uid in available if miners[uid]["task_availabilities"].get(task_name, False)] if model: - available = [uid for uid in available if self.miners[uid].is_model_available(model)] + available = [uid for uid in available if miners[uid]["llm_model_availabilities"].get(model, False)] if k: available = random.sample(available, min(len(available), k)) return list(map(int, available)) @@ -59,10 +42,16 @@ class CheckMinerAvailability(AsyncLoopRunner): uids: np.ndarray = shared_settings.TEST_MINER_IDS or get_uids(sampling_mode="all") current_index: int = 0 uids_per_step: int = 10 + miners_dict: dict[int, dict] = {} class Config: arbitrary_types_allowed = True + async def start(self, miners_dict: dict[int, dict], **kwargs): + self.miners_dict = miners_dict + logger.debug("Starting availability checking loop...") + return await super().start(**kwargs) + async def run_step(self): start_index = self.current_index end_index = min(start_index + self.uids_per_step, len(self.uids)) @@ -76,23 +65,23 @@ async def run_step(self): for response, uid in zip(responses, uids_to_query): try: - miner_availabilities.miners[uid] = MinerAvailability( - task_availabilities=response["task_availabilities"], - llm_model_availabilities=response["llm_model_availabilities"], - ) + self.miners_dict[uid] = { + "task_availabilities": response["task_availabilities"], + "llm_model_availabilities": response["llm_model_availabilities"], + } except Exception: # logger.debug("Availability Response Invalid") - miner_availabilities.miners[uid] = MinerAvailability( - task_availabilities={task: True for task in task_config}, - llm_model_availabilities={model: False for model in model_config}, - ) + self.miners_dict[uid] = { + "task_availabilities": {task: True for task in task_config}, + "llm_model_availabilities": {model: False for model in model_config}, + } self.current_index = end_index if self.current_index >= len(self.uids): self.current_index = 0 - tracked_miners = [m for m in miner_availabilities.miners.values() if m is not None] + tracked_miners = [m for m in self.miners_dict.values() if m is not None] logger.debug( f"TRACKED MINERS: {len(tracked_miners)} --- UNTRACKED MINERS: {len(self.uids) - len(tracked_miners)}" ) @@ -101,5 +90,6 @@ async def run_step(self): await asyncio.sleep(0.1) -miner_availabilities = MinerAvailabilities() +# Initialize global miners dictionary +# miners_dict: dict[int, dict] = {} availability_checking_loop = CheckMinerAvailability() diff --git a/prompting/rewards/exact_match.py b/prompting/rewards/exact_match.py index d9570e506..d15afdd59 100644 --- a/prompting/rewards/exact_match.py +++ b/prompting/rewards/exact_match.py @@ -6,7 +6,7 @@ from shared.dendrite import DendriteResponseEvent shared_settings = settings.shared_settings -INCORRECT_PENALTY = 3 +INCORRECT_PENALTY = 1 INCOMPLETE_PENALTY = 1 diff --git a/prompting/rewards/scoring.py b/prompting/rewards/scoring.py index 806aa4110..4cc13ec90 100644 --- a/prompting/rewards/scoring.py +++ b/prompting/rewards/scoring.py @@ -12,6 +12,7 @@ from shared.dendrite import DendriteResponseEvent from shared.logging import RewardLoggingEvent, log_event from shared.loop_runner import AsyncLoopRunner +from shared.timer import Timer class TaskScorer(AsyncLoopRunner): @@ -27,10 +28,10 @@ class TaskScorer(AsyncLoopRunner): model_config = ConfigDict(arbitrary_types_allowed=True) - async def start(self, scoring_queue, reward_events, name: str | None = None): + async def start(self, scoring_queue, reward_events, name: str | None = None, **kwargs): self.scoring_queue = scoring_queue self.reward_events = reward_events - return await super().start(name=name) + return await super().start(name=name, **kwargs) def add_to_queue( self, @@ -70,19 +71,21 @@ async def run_step(self) -> RewardLoggingEvent: scoring_config: ScoringConfig = scorable.pop(0) # here we generate the actual reference - await scoring_config.task.make_reference( - dataset_entry=scoring_config.dataset_entry, - ) + with Timer(label=f"Generating reference for {scoring_config.task.__class__.__name__}"): + await scoring_config.task.make_reference( + dataset_entry=scoring_config.dataset_entry, + ) # and there we then calculate the reward reward_pipeline = TaskRegistry.get_task_reward(scoring_config.task) - reward_events = await reward_pipeline.apply( - response_event=scoring_config.response, - challenge=scoring_config.task.query, - reference=scoring_config.task.reference, - model_id=scoring_config.task.llm_model, - task=scoring_config.task, - ) + with Timer(label=f"Scoring {scoring_config.task.__class__.__name__}"): + reward_events = await reward_pipeline.apply( + response_event=scoring_config.response, + challenge=scoring_config.task.query, + reference=scoring_config.task.reference, + model_id=scoring_config.task.llm_model, + task=scoring_config.task, + ) self.reward_events.append(reward_events) # TODO: Remove this once we have a better way to handle organic tasks diff --git a/prompting/tasks/base_task.py b/prompting/tasks/base_task.py index 637bde9d1..b2ccac70a 100644 --- a/prompting/tasks/base_task.py +++ b/prompting/tasks/base_task.py @@ -37,7 +37,7 @@ class BaseTask(BaseModel, ABC): model_config = ConfigDict(arbitrary_types_allowed=True) @abstractmethod - def make_query(self, **kwargs): + async def make_query(self, **kwargs): raise NotImplementedError("Method make_query must be implemented") @abstractmethod @@ -72,15 +72,15 @@ def get_model_id_and_seed(self) -> "BaseTextTask": self.llm_model_id = self.llm_model.llm_model_id if self.llm_model else None return self - def make_query(self, dataset_entry: DatasetEntry, **kwargs) -> str: + async def make_query(self, dataset_entry: DatasetEntry, **kwargs) -> str: return self.query async def make_reference(self, dataset_entry: DatasetEntry) -> str: return self.reference - def generate_reference(self, messages: list[str]) -> str: + async def generate_reference(self, messages: list[str]) -> str: """Generates a reference answer to be used for scoring miner completions""" - self.reference = model_manager.get_model(settings.shared_settings.LLM_MODEL[0]).generate( + self.reference = await model_manager.get_model(settings.shared_settings.LLM_MODEL[0]).generate( messages=messages ) # This should be a list of dict if self.reference is None: @@ -88,10 +88,7 @@ def generate_reference(self, messages: list[str]) -> str: return self.reference - def generate_query( - self, - messages: list[str], - ) -> str: + async def generate_query(self, messages: list[str]) -> str: """Generates a query to be used for generating the challenge""" llm_messages = [LLMMessage(role="system", content=self.query_system_prompt)] if self.query_system_prompt else [] llm_messages.extend([LLMMessage(role="user", content=message) for message in messages]) diff --git a/prompting/tasks/inference.py b/prompting/tasks/inference.py index 1170ef2a3..640274682 100644 --- a/prompting/tasks/inference.py +++ b/prompting/tasks/inference.py @@ -8,7 +8,7 @@ from prompting.llms.model_manager import model_manager from prompting.llms.model_zoo import ModelConfig, ModelZoo from prompting.rewards.inference_reward_model import InferenceRewardModel -from prompting.rewards.penalty import PenaltyModel +from prompting.rewards.relevance import RelevanceRewardModel from prompting.rewards.reward import BaseRewardConfig, BaseRewardModel from prompting.tasks.base_task import BaseTextTask from shared import settings @@ -19,7 +19,7 @@ class InferenceRewardConfig(BaseRewardConfig): reward_definitions: ClassVar[list[BaseRewardModel]] = [ InferenceRewardModel(weight=0.5), - PenaltyModel(weight=0.5), + RelevanceRewardModel(weight=0.5), ] @@ -65,7 +65,7 @@ def random_llm_model_id(self): self.llm_model = ModelZoo.get_model_by_id(self.llm_model_id) return self - def make_query(self, dataset_entry: ChatEntry) -> str: + async def make_query(self, dataset_entry: ChatEntry) -> str: if self.query: return self.query system_prompt = random.choice(SYSTEM_PROMPTS) @@ -76,7 +76,7 @@ def make_query(self, dataset_entry: ChatEntry) -> str: return self.query async def make_reference(self, dataset_entry: ChatEntry) -> str: - self.reference = model_manager.generate( + self.reference = await model_manager.generate( messages=self.messages, model=self.llm_model, seed=self.seed, diff --git a/prompting/tasks/multi_choice.py b/prompting/tasks/multi_choice.py deleted file mode 100644 index 4598970fb..000000000 --- a/prompting/tasks/multi_choice.py +++ /dev/null @@ -1,205 +0,0 @@ -import json -import random -from typing import ClassVar - -import numpy as np - -from prompting.rewards.multi_choice import MultiChoiceRewardModel -from prompting.rewards.reward import BaseRewardConfig, BaseRewardModel -from prompting.tasks.base_task import BaseTextTask -from shared.base import Context -from shared.exceptions import TaskCreationError - -# TODO: Introduce criteria for the query and reference answer (length, layout, etc.) and make these arguments. - -MINER_EXAMPLE_1_SHOT = """\ -[Example 1] -What is the capital of Texas? -A. Paris -B. London -C. Austin -D. Houston -Answer: C -""" - -# Used to instruct the LLM to provide a query when given a context. -QUERY_SYSTEM_PROMPT = """Given the following input context, create a multiple-choice question based on the information provided. The question must have one correct answer and three incorrect answers. -Ensure the following: -1. The correct answer is derived from the input context. -2. All answer choices should have roughly the same character length. No answer should significantly stand out as longer or shorter. -3. The correct answer should not consistently be the longest option; it should only be the longest about 25% of the time. -4. Randomize answer length distribution across multiple samples. -5. The output format must match the example's output format. -[Example 1] -{ - "question": "Which of the following is not an element of the redistribution-with-growth policy approach?", - "A": "minimum wage legislation", - "B": "land reform", - "C": "progressive taxation", - "D": "increased access to education", - "answer": "A" -} -[Example 2] -{ - "question": "Which of the following best describes the primary driving force behind protein folding?", - "A": "Covalent bond formation between amino acids", - "B": "Hydrophobic interactions between nonpolar side chains", - "C": "Hydrogen bonds between the protein backbone and side chains", - "D": "Ionic interactions between charged side chains", - "answer": "B" -} -[Example 3] -{ - "question": "What is the capital of Texas?", - "A": "Paris", - "B": "London", - "C": "Austin", - "D": "Houston", - "answer": "C" -} -[Example 4] -{ - "question": "What interior discipline must be adopted to achieve spiritual liberation within Sikhism?", - "A": "Remembering the Divine Name", - "B": "Meditating on the sacred hymns", - "C": "Remembering that death is inevitable", - "D": "Meditating on the goodness of the created world", - "answer": "A" -}""" - -# Used to obtain the query (which is a question about the context). -# TODO: modulate difficulty "ask an {expert} question". -QUERY_PROMPT_TEMPLATE = """\ -Create a multiple choice quiz based on the following context source from {source} about {title}: - -[Input Context] -{context} -""" - - -class MultiChoiceRewardConfig(BaseRewardConfig): - reward_definitions: ClassVar[list[BaseRewardModel]] = [ - MultiChoiceRewardModel(weight=1.0), - ] - - -class MultiChoiceTask(BaseTextTask): - name: ClassVar[str] = "multi_choice" - query_system_prompt: ClassVar[str] = QUERY_SYSTEM_PROMPT - augmentation_system_prompt: ClassVar[str] = "" - llm_model_id: str | None = None - - # Specific pattern (semi-flexible) which detects multiple choices. - choices_pattern: ClassVar[str] = r"\n\s*(\*?\s*\W?[A-D]\W?)\s*(.*)" - - def make_query(self, dataset_entry: Context) -> tuple[str, str]: - query_prompt = QUERY_PROMPT_TEMPLATE.format( - source=dataset_entry.source, title=dataset_entry.title, context=dataset_entry.content - ) - query_with_choices = self.generate_query(messages=[query_prompt]) - self.query, self.reference = self.extract_query_and_reference(query_with_choices) - self.query = self.post_process_qa(self.query) - return self.query - - def post_process_qa(self, query: str) -> str: - options = query.split("?")[2].split("\n") - cleaned_options = [item.strip() for item in options if item.strip() and item.strip() != "Answer:"] - letter_to_index = {"A": 0, "B": 1, "C": 2, "D": 3} - try: - int(cleaned_options[letter_to_index.get(self.reference)].split(". ")[1]) - except Exception: - return query - new_idx = random.randint(0, 3) - answer = int(cleaned_options[letter_to_index.get(self.reference)].split(". ")[1]) - step = random.randint(1, 10) - new_options = [int(answer) + (i - new_idx) * step for i in range(4)] - new_options = [opt for opt in new_options if opt != answer] - letter_options = ["A. ", "B. ", "C. ", "D. "] - available_letters = [opt for opt in letter_options if f"{self.reference}. " not in opt] - random.shuffle(available_letters) - random.shuffle(new_options) - new_options = [available_letters[i] + str(new_options[i]) for i in range(3)] - new_options.append(self.reference + ". " + str(answer)) - new_options = sorted(new_options, key=lambda x: x.split(". ")[0]) - new_options.append("Answer:") - options_string = "\n".join(new_options) - new_query = "?".join(query.split("?")[:2]) + "?\n" + options_string - return new_query - - async def make_reference(self, dataset_entry: Context) -> str: - return self.reference - - def extract_query_and_reference(self, query_with_choices: str) -> tuple[str, str]: - """ - Detects JSON within a string, parses it into a dictionary, - and validates that the dictionary contains the required fields: - "question", "answer", "A", "B", "C", and "D". - - Args: - json_string (str): The string containing the JSON data, possibly with extra text. - - Returns: - dict: The parsed and validated dictionary. - - Raises: - ValueError: If JSON extraction or parsing fails, or required fields are missing. - """ - - # Regular expression pattern to match JSON object in the string. - def extract_json_from_string(string: str): - start = string.find("{") - end = string.rfind("}") + 1 - if start != -1 and end != -1: - json_string = string[start:end] - try: - return json.loads(json_string) - except json.JSONDecodeError: - pass - return None - - quiz_data = extract_json_from_string(query_with_choices) - if not quiz_data: - raise TaskCreationError(f"No JSON object could be found in the provided string: {query_with_choices}.") - - required_fields = ["question", "answer", "A", "B", "C", "D"] - - # Check for missing fields. - for field in required_fields: - if field not in quiz_data: - raise TaskCreationError(f"Missing required field: '{field}'") - - # Answer must be exactly one of the choices. - if quiz_data["answer"] not in ("A", "B", "C", "D"): - raise TaskCreationError(f"Invalid answer: '{quiz_data['answer']}'") - - quiz, reference = self.shuffle_and_format(quiz_data) - return quiz, reference - - def shuffle_and_format(self, quiz_data: dict[str, str]) -> tuple[str, str]: - """Shuffles the choices and formats them into a string with the question. - - Args: - quiz_data (dict): The dictionary containing the quiz data. - - Returns: - str: The formatted string with the question and shuffled choices. - """ - # Extract choices and the correct answer. - choices = ["A", "B", "C", "D"] - choice_texts = [quiz_data[choice] for choice in choices] - correct_answer = quiz_data["answer"] - - # Shuffle the choices. - shuffled_choices = list(zip(choices, np.random.permutation(choice_texts))) - - # Determine the new correct answer after shuffling. - new_reference = [choice for choice, text in shuffled_choices if text == quiz_data[correct_answer]][0] - - # Format the shuffled question and choices. - prompt: list[str] = [] - prompt.append(f"{MINER_EXAMPLE_1_SHOT}\n") - prompt.append(f"[Input Question]\n{quiz_data['question']}\n\n") - prompt.append("\n".join([f"{choice}. {text}" for choice, text in shuffled_choices])) - prompt.append("\nAnswer: ") - - return "".join(prompt), new_reference diff --git a/prompting/tasks/multi_step_reasoning.py b/prompting/tasks/multi_step_reasoning.py index e00049f74..2d8851415 100644 --- a/prompting/tasks/multi_step_reasoning.py +++ b/prompting/tasks/multi_step_reasoning.py @@ -6,7 +6,7 @@ from prompting.datasets.random_website import DDGDatasetEntry from prompting.rewards.relevance import RelevanceRewardModel from prompting.rewards.reward import BaseRewardConfig, BaseRewardModel -from prompting.tasks.qa import WikiQuestionAnsweringTask +from prompting.tasks.qa import WebQuestionAnsweringTask from shared.base import Context from validator_api.test_time_inference import generate_response @@ -71,7 +71,7 @@ class MultiStepReasoningRewardConfig(BaseRewardConfig): ] -class MultiStepReasoningTask(WikiQuestionAnsweringTask): +class MultiStepReasoningTask(WebQuestionAnsweringTask): """QuestionAnsweringTasks must be initialised with an LLM pipeline to generate query and reference plus context from a dataset to base the query on""" @@ -81,9 +81,9 @@ class MultiStepReasoningTask(WikiQuestionAnsweringTask): query_system_prompt: str = QUERY_SYSTEM_PROMPT reference: str | None = None - def make_query(self, dataset_entry: DDGDatasetEntry): + async def make_query(self, dataset_entry: DDGDatasetEntry): query_prompt = QUERY_PROMPT_TEMPLATE.format(context=dataset_entry.website_content) - question = self.generate_query(messages=[query_prompt]) + question = await self.generate_query(messages=[query_prompt]) msgs = [p + ". " if i < len(question.split(". ")) - 1 else p for i, p in enumerate(question.split(". ")) if p] self.messages = [{"role": "system", "content": random.choice(SAMPLE_SYSTEM_PROMPTS)}] + [ {"role": random.choice(["user", "assistant"]), "content": msg} for msg in msgs diff --git a/prompting/tasks/programming_task.py b/prompting/tasks/programming_task.py index ea08886f4..e52144815 100644 --- a/prompting/tasks/programming_task.py +++ b/prompting/tasks/programming_task.py @@ -37,7 +37,7 @@ class ProgrammingTask(BaseTextTask): query: str | None = None reference: str | None = None - def make_query(self, dataset_entry: HuggingFaceGithubDatasetEntry): + async def make_query(self, dataset_entry: HuggingFaceGithubDatasetEntry): modified_code = LLMWrapper.chat_complete( messages=LLMMessages( LLMMessage( diff --git a/prompting/tasks/qa.py b/prompting/tasks/qa.py index d9f4285e4..fd6143e41 100644 --- a/prompting/tasks/qa.py +++ b/prompting/tasks/qa.py @@ -5,7 +5,7 @@ from prompting.rewards.reward import BaseRewardConfig, BaseRewardModel from prompting.rewards.rouge import RougeRewardModel from prompting.tasks.base_task import BaseTextTask -from shared.base import Context +from shared.base import Context # type: ignore # noqa: F401 # Used to instruct the LLM to provide a good query when given a context QUERY_SYSTEM_PROMPT = """\ @@ -48,28 +48,6 @@ class QARewardConfig(BaseRewardConfig): penalty_definition: ClassVar[list[BaseRewardModel]] = [RougeRewardModel(weight=0.5)] -class WikiQuestionAnsweringTask(BaseTextTask): - """QuestionAnsweringTasks must be initialised with an LLM pipeline to generate query and reference plus - context from a dataset to base the query on""" - - name: ClassVar[str] = "wiki_qa" - query_system_prompt: ClassVar[str] = QUERY_SYSTEM_PROMPT - reference_system_prompt: ClassVar[str] = REFERENCE_SYSTEM_PROMPT - augmentation_system_prompt: ClassVar[str] = "" - query: str | None = None - reference: str | None = None - - def make_query(self, dataset_entry: Context): - query_prompt = QUERY_PROMPT_TEMPLATE.format(context=dataset_entry.content) - self.query = self.generate_query(messages=[query_prompt]) - return self.query - - async def make_reference(self, dataset_entry: Context): - reference_prompt = REFERENCE_PROMPT_TEMPLATE.format(context=dataset_entry.content, question=self.query) - self.reference = self.generate_reference(messages=[{"role": "user", "content": reference_prompt}]) - return self.reference - - class WebQuestionAnsweringTask(BaseTextTask): """QuestionAnsweringTasks must be initialised with an LLM pipeline to generate query and reference plus context from a dataset to base the query on""" @@ -81,12 +59,12 @@ class WebQuestionAnsweringTask(BaseTextTask): query: str | None = None reference: str | None = None - def make_query(self, dataset_entry: DDGDatasetEntry): + async def make_query(self, dataset_entry: DDGDatasetEntry): query_prompt = QUERY_PROMPT_TEMPLATE.format(context=dataset_entry.website_content) - self.query = self.generate_query(messages=[query_prompt]) + self.query = await self.generate_query(messages=[query_prompt]) return self.query async def make_reference(self, dataset_entry: DDGDatasetEntry): reference_prompt = REFERENCE_PROMPT_TEMPLATE.format(context=dataset_entry.website_content, question=self.query) - self.reference = self.generate_reference(messages=[{"role": "user", "content": reference_prompt}]) + self.reference = await self.generate_reference(messages=[{"role": "user", "content": reference_prompt}]) return self.reference diff --git a/prompting/tasks/task_creation.py b/prompting/tasks/task_creation.py index 15cc03256..459be013d 100644 --- a/prompting/tasks/task_creation.py +++ b/prompting/tasks/task_creation.py @@ -4,12 +4,13 @@ from loguru import logger from pydantic import ConfigDict -from prompting.miner_availability.miner_availability import miner_availabilities +from prompting.miner_availability.miner_availability import MinerAvailabilities from prompting.tasks.task_registry import TaskRegistry from shared import settings # from shared.logging import ErrorLoggingEvent, ValidatorLoggingEvent from shared.loop_runner import AsyncLoopRunner +from shared.timer import Timer shared_settings = settings.shared_settings @@ -22,12 +23,14 @@ class TaskLoop(AsyncLoopRunner): interval: int = 0 task_queue: list | None = [] scoring_queue: list | None = [] + miners_dict: dict | None = None model_config = ConfigDict(arbitrary_types_allowed=True) - async def start(self, task_queue, scoring_queue): + async def start(self, task_queue, scoring_queue, miners_dict, **kwargs): self.task_queue = task_queue self.scoring_queue = scoring_queue - await super().start() + self.miners_dict = miners_dict + await super().start(**kwargs) async def run_step(self): if len(self.task_queue) > shared_settings.TASK_QUEUE_LENGTH_THRESHOLD: @@ -48,7 +51,14 @@ async def run_step(self): logger.exception(ex) await asyncio.sleep(0.1) - if len(miner_availabilities.get_available_miners(task=task, model=task.llm_model_id)) == 0: + if ( + len( + MinerAvailabilities.get_available_miners( + miners=self.miners_dict, task=task, model=task.llm_model_id + ) + ) + == 0 + ): logger.debug( f"No available miners for Task: {task.__class__.__name__} and Model ID: {task.llm_model_id}. Skipping step." ) @@ -58,10 +68,11 @@ async def run_step(self): logger.warning(f"Dataset for task {task.__class__.__name__} returned None. Skipping step.") return None - # Generate the query and reference for the task - if not task.query: - logger.debug(f"Generating query for task: {task.__class__.__name__}.") - task.make_query(dataset_entry=dataset_entry) + # Generate the query for the task + with Timer(label=f"Generating query for task: {task.__class__.__name__}"): + if not task.query: + logger.debug(f"Generating query for task: {task.__class__.__name__}.") + await task.make_query(dataset_entry=dataset_entry) logger.debug(f"Generated Messages: {task.task_messages}") logger.debug(f"Appending task: {task.__class__.__name__} to task queue.") diff --git a/prompting/tasks/task_registry.py b/prompting/tasks/task_registry.py index 7ce41ce85..3ee0d27ca 100644 --- a/prompting/tasks/task_registry.py +++ b/prompting/tasks/task_registry.py @@ -8,14 +8,12 @@ from prompting.datasets.huggingface_github import HuggingFaceGithubDataset from prompting.datasets.random_website import DDGDataset from prompting.datasets.sn13 import SN13Dataset -from prompting.datasets.wiki import WikiDataset from prompting.rewards.reward import BaseRewardConfig from prompting.tasks.base_task import BaseTextTask from prompting.tasks.inference import InferenceRewardConfig, InferenceTask -from prompting.tasks.multi_choice import MultiChoiceRewardConfig, MultiChoiceTask from prompting.tasks.multi_step_reasoning import MultiStepReasoningRewardConfig, MultiStepReasoningTask from prompting.tasks.programming_task import ProgrammingRewardConfig, ProgrammingTask -from prompting.tasks.qa import QARewardConfig, WebQuestionAnsweringTask, WikiQuestionAnsweringTask +from prompting.tasks.qa import QARewardConfig, WebQuestionAnsweringTask from prompting.tasks.web_retrieval import WebRetrievalRewardConfig, WebRetrievalTask from shared.base import BaseDataset @@ -34,22 +32,13 @@ def __hash__(self): class TaskRegistry(BaseModel): task_configs: ClassVar[list[TaskConfig]] = [ - TaskConfig( - task=WikiQuestionAnsweringTask, probability=0.05, datasets=[WikiDataset], reward_model=QARewardConfig - ), - TaskConfig(task=WebQuestionAnsweringTask, probability=0.15, datasets=[DDGDataset], reward_model=QARewardConfig), + TaskConfig(task=WebQuestionAnsweringTask, probability=0.05, datasets=[DDGDataset], reward_model=QARewardConfig), TaskConfig( task=InferenceTask, probability=0.3, datasets=[SN13Dataset], reward_model=InferenceRewardConfig, ), - TaskConfig( - task=MultiChoiceTask, - probability=0.2, - datasets=[WikiDataset], - reward_model=MultiChoiceRewardConfig, - ), TaskConfig( task=ProgrammingTask, probability=0.10, @@ -58,13 +47,13 @@ class TaskRegistry(BaseModel): ), TaskConfig( task=WebRetrievalTask, - probability=0.1, + probability=0.25, datasets=[DDGDataset], reward_model=WebRetrievalRewardConfig, ), TaskConfig( task=MultiStepReasoningTask, - probability=0.1, + probability=0.3, datasets=[DDGDataset], reward_model=MultiStepReasoningRewardConfig, ), diff --git a/prompting/tasks/task_sending.py b/prompting/tasks/task_sending.py index c21bd204a..d77fdc499 100644 --- a/prompting/tasks/task_sending.py +++ b/prompting/tasks/task_sending.py @@ -5,7 +5,7 @@ import bittensor as bt from loguru import logger -from prompting.miner_availability.miner_availability import miner_availabilities +from prompting.miner_availability.miner_availability import MinerAvailabilities # from prompting.rewards.scoring import task_scorer from prompting.rewards.scoring_config import ScoringConfig @@ -40,9 +40,11 @@ def log_stream_results(stream_results): logger.debug(f"Total of failed responses: ({len(failed_responses)})") -async def collect_responses(task: BaseTextTask) -> DendriteResponseEvent | None: +async def collect_responses(task: BaseTextTask, miners_dict: dict) -> DendriteResponseEvent | None: # Get the list of uids and their axons to query for this step. - uids = miner_availabilities.get_available_miners(task=task, model=task.llm_model_id, k=NEURON_SAMPLE_SIZE) + uids = MinerAvailabilities.get_available_miners( + miners=miners_dict, task=task, model=task.llm_model_id, k=NEURON_SAMPLE_SIZE + ) if len(uids) == 0: logger.warning("No available miners. This should already have been caught earlier.") return @@ -82,17 +84,19 @@ class TaskSender(AsyncLoopRunner): task_queue: list | None = None scoring_queue: list | None = None subtensor: bt.Subtensor | None = None + miners_dict: dict | None = None class Config: arbitrary_types_allowed = True - async def start(self, task_queue, scoring_queue): + async def start(self, task_queue, scoring_queue, miners_dict, **kwargs): self.task_queue = task_queue self.scoring_queue = scoring_queue + self.miners_dict = miners_dict # shared_settings is not initialised inside this process, meaning it cannot access any non-constants from here self.subtensor = bt.subtensor(network=shared_settings.SUBTENSOR_NETWORK) - return await super().start() + return await super().start(**kwargs) @property def block(self): @@ -134,7 +138,7 @@ async def run_step(self) -> ValidatorLoggingEvent | ErrorLoggingEvent | None: timeout (float): The timeout for the queries. exclude (list, optional): The list of uids to exclude from the query. Defaults to []. """ - # logger.info(f"Checking for tasks to be sent...") + logger.info("Checking for tasks to be sent...") while len(self.scoring_queue) > shared_settings.SCORING_QUEUE_LENGTH_THRESHOLD: await asyncio.sleep(1) while len(self.task_queue) == 0: @@ -145,8 +149,8 @@ async def run_step(self) -> ValidatorLoggingEvent | ErrorLoggingEvent | None: task = self.task_queue.pop(0) # send the task to the miners and collect the responses - with Timer() as timer: - response_event = await collect_responses(task=task) + with Timer(label=f"Sending {task.__class__.__name__}") as timer: + response_event = await collect_responses(task=task, miners_dict=self.miners_dict) if response_event is None: return diff --git a/prompting/tasks/web_retrieval.py b/prompting/tasks/web_retrieval.py index f0cb10040..69dbf901a 100644 --- a/prompting/tasks/web_retrieval.py +++ b/prompting/tasks/web_retrieval.py @@ -41,8 +41,8 @@ class WebRetrievalTask(BaseTextTask): target_results: int = Field(default_factory=lambda: random.randint(1, 10)) timeout: int = Field(default_factory=lambda: random.randint(5, 20)) - def make_query(self, dataset_entry: DDGDatasetEntry) -> str: - self.query = self.generate_query( + async def make_query(self, dataset_entry: DDGDatasetEntry) -> str: + self.query = await self.generate_query( messages=[MESSAGE_TEMPLATE.format(website_content=dataset_entry.website_content)] ) return self.query diff --git a/prompting/weight_setting/weight_setter.py b/prompting/weight_setting/weight_setter.py index 977fa3303..339c7d17d 100644 --- a/prompting/weight_setting/weight_setter.py +++ b/prompting/weight_setting/weight_setter.py @@ -142,7 +142,7 @@ class WeightSetter(AsyncLoopRunner): class Config: arbitrary_types_allowed = True - async def start(self, reward_events, name: str | None = None): + async def start(self, reward_events, name: str | None = None, **kwargs): self.reward_events = reward_events global PAST_WEIGHTS @@ -154,7 +154,7 @@ async def start(self, reward_events, name: str | None = None): PAST_WEIGHTS = [] except Exception as ex: logger.error(f"Couldn't load weights from file: {ex}") - return await super().start(name=name) + return await super().start(name=name, **kwargs) async def run_step(self): await asyncio.sleep(0.01) diff --git a/pyproject.toml b/pyproject.toml index b97fd0ecd..f78560860 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "prompting" -version = "2.17.7" +version = "2.17.8" description = "Subnetwork 1 runs on Bittensor and is maintained by Macrocosmos. It's an effort to create decentralised AI" authors = ["Kalei Brady, Dmytro Bobrenko, Felix Quinque, Steffen Cruz, Richard Wardle"] readme = "README.md" diff --git a/shared/base.py b/shared/base.py index 5f08d1272..2f34e05e5 100644 --- a/shared/base.py +++ b/shared/base.py @@ -63,7 +63,7 @@ def next(self, method: Literal["random", "search", "get"] = "random", **kwargs) tries = 1 context: DatasetEntry # for some reason the ls doesn't understand it's of type Context without this - with Timer() as timer: + with Timer(label=f"Fetching Data from {self.__class__.__name__} Dataset") as timer: for _ in range(RETRIES): # TODO: Multithread the get method so that we don't have to suffer nonexistent pages if method == "random": diff --git a/shared/loop_runner.py b/shared/loop_runner.py index 617b39c3a..51efdfad2 100644 --- a/shared/loop_runner.py +++ b/shared/loop_runner.py @@ -2,6 +2,7 @@ import datetime from abc import ABC, abstractmethod from datetime import timedelta +from typing import List import aiohttp from loguru import logger @@ -17,6 +18,7 @@ class AsyncLoopRunner(BaseModel, ABC): time_server_url: str = "http://worldtimeapi.org/api/ip" name: str | None = None step: int = 0 + _tasks: List[asyncio.Task] = [] @model_validator(mode="after") def validate_name(self): @@ -46,7 +48,7 @@ async def get_time(self): logger.warning(f"Could not get time from server: {ex}. Falling back to local time.") return datetime.datetime.now(datetime.timezone.utc) - def next_sync_point(self, current_time): + async def next_sync_point(self, current_time): """Calculate the next sync point based on the current time and interval.""" epoch = datetime.datetime(1970, 1, 1, tzinfo=datetime.timezone.utc) time_since_epoch = current_time - epoch @@ -60,7 +62,7 @@ async def wait_for_next_execution(self, last_run_time): if last_run_time.tzinfo is None: last_run_time = last_run_time.replace(tzinfo=current_time.tzinfo) if self.sync: - next_run = self.next_sync_point(current_time) + next_run = await self.next_sync_point(current_time) else: next_run = last_run_time + timedelta(seconds=self.interval) @@ -86,23 +88,39 @@ async def run_loop(self): logger.info("Loop was stopped.") self.running = False except Exception as e: - logger.error(f"Fatal error in loop: {e}") + logger.exception(f"Fatal error in loop: {e}") self.running = False - async def start(self, name: str | None = None): - """Start the loop.""" - if self.running: - logger.warning("Loop is already running.") - return - self.running = True - self._task = asyncio.create_task(self.run_loop(), name=name) + async def start(self, name: str | None = None, simultaneous_loops: int = 1, **kwargs): + """Start the loop with optional multiple simultaneous instances. + + Args: + name: Optional name for the loop tasks + simultaneous_loops: Number of simultaneous loop instances to run (default: 1) + """ + try: + if self.running: + logger.warning("Loop is already running.") + return + + self.running = True + self._tasks = [] + + for i in range(simultaneous_loops): + task_name = f"{name}_{i}" if name else f"{self.name}_{i}" + task = asyncio.create_task(self.run_loop(), name=task_name) + self._tasks.append(task) + except Exception as e: + logger.exception(f"Error in start method: {e}") async def stop(self): - """Stop the loop.""" + """Stop all running loops.""" self.running = False - if self._task: - self._task.cancel() + if self._tasks: + for task in self._tasks: + task.cancel() try: - await self._task + await asyncio.gather(*self._tasks, return_exceptions=True) except asyncio.CancelledError: - logger.debug("Loop task was cancelled.") + logger.debug("Loop tasks were cancelled.") + self._tasks = [] diff --git a/shared/settings.py b/shared/settings.py index 6164348b6..5dfb08d5c 100644 --- a/shared/settings.py +++ b/shared/settings.py @@ -14,6 +14,7 @@ import bittensor as bt import dotenv +from bittensor.core.metagraph import Metagraph from loguru import logger from pydantic import Field, model_validator from pydantic_settings import BaseSettings @@ -28,11 +29,13 @@ class SharedSettings(BaseSettings): _instance: Optional["SharedSettings"] = None _instance_mode: Optional[str] = None + _last_metagraph: Metagraph = None mode: Literal["api", "validator", "miner", "mock"] = Field("validator", env="MODE") MOCK: bool = False NO_BACKGROUND_THREAD: bool = True SAVE_PATH: Optional[str] = Field("./storage", env="SAVE_PATH") + GEMMA_API_KEY: Optional[str] = Field(None, env="GEMMA_API_KEY") # W&B. WANDB_ON: bool = Field(True, env="WANDB_ON") @@ -52,6 +55,7 @@ class SharedSettings(BaseSettings): # Logging. LOGGING_DONT_SAVE_EVENTS: bool = Field(True, env="LOGGING_DONT_SAVE_EVENTS") LOG_WEIGHTS: bool = Field(False, env="LOG_WEIGHTS") + LOG_TIMINGS: bool = Field(False, env="LOG_TIMINGS") # Neuron parameters. NEURON_TIMEOUT: int = Field(20, env="NEURON_TIMEOUT") @@ -79,8 +83,8 @@ class SharedSettings(BaseSettings): ORGANIC_TRIGGER_FREQUENCY_MIN: int = Field(5, env="ORGANIC_TRIGGER_FREQUENCY_MIN") ORGANIC_TRIGGER: str = Field("seconds", env="ORGANIC_TRIGGER") ORGANIC_SCALING_FACTOR: int = Field(1, env="ORGANIC_SCALING_FACTOR") - TASK_QUEUE_LENGTH_THRESHOLD: int = Field(10, env="TASK_QUEUE_LENGTH_THRESHOLD") - SCORING_QUEUE_LENGTH_THRESHOLD: int = Field(10, env="SCORING_QUEUE_LENGTH_THRESHOLD") + TASK_QUEUE_LENGTH_THRESHOLD: int = Field(50, env="TASK_QUEUE_LENGTH_THRESHOLD") + SCORING_QUEUE_LENGTH_THRESHOLD: int = Field(50, env="SCORING_QUEUE_LENGTH_THRESHOLD") HF_TOKEN: Optional[str] = Field(None, env="HF_TOKEN") DEPLOY_VALIDATOR: bool = Field(True, env="DEPLOY_VALDITAOR") DEPLOY_SCORING_API: bool = Field(True, env="DEPLOY_SCORING_API") @@ -245,6 +249,7 @@ def complete_settings(cls, values: dict[str, Any]) -> dict[str, Any]: @cached_property def WALLET(self): + # TODO: Move chain-related stuff out of settings. wallet_name = self.WALLET_NAME # or config().wallet.name hotkey = self.HOTKEY # or config().wallet.hotkey logger.info(f"Instantiating wallet with name: {wallet_name}, hotkey: {hotkey}") @@ -252,6 +257,7 @@ def WALLET(self): @cached_property def SUBTENSOR(self) -> bt.subtensor: + # TODO: Move chain-related stuff out of settings. subtensor_network = self.SUBTENSOR_NETWORK or os.environ.get("SUBTENSOR_NETWORK", "local") # bt_config = config() if subtensor_network.lower() == "local": @@ -262,16 +268,30 @@ def SUBTENSOR(self) -> bt.subtensor: return bt.subtensor(network=subtensor_network) @cached_property_with_expiration(expiration_seconds=1200) - def METAGRAPH(self) -> bt.metagraph: + def METAGRAPH(self) -> Metagraph: + # TODO: Move chain-related stuff out of settings. logger.info(f"Instantiating metagraph with NETUID: {self.NETUID}") - return self.SUBTENSOR.metagraph(netuid=self.NETUID) + try: + meta = self.SUBTENSOR.metagraph(netuid=self.NETUID) + self._last_metagraph = meta + return meta + except Exception as e: + logger.error(f"Failed to fetch new METAGRAPH for NETUID={self.NETUID}: {e}") + if self._last_metagraph is not None: + logger.warning("Falling back to the previous METAGRAPH.") + return self._last_metagraph + else: + logger.error("No previous METAGRAPH is available; re-raising exception.") + raise @cached_property def UID(self) -> int: + # TODO: Move chain-related stuff out of settings. return self.METAGRAPH.hotkeys.index(self.WALLET.hotkey.ss58_address) @cached_property def DENDRITE(self) -> bt.dendrite: + # TODO: Move chain-related stuff out of settings. logger.info(f"Instantiating dendrite with wallet: {self.WALLET}") return bt.dendrite(wallet=self.WALLET) diff --git a/shared/timer.py b/shared/timer.py index 4fa054960..725a86c01 100644 --- a/shared/timer.py +++ b/shared/timer.py @@ -1,14 +1,41 @@ +import csv +import os import time +from datetime import datetime + +from shared import settings + +shared_settings = settings.shared_settings + +# Create log file name when module is loaded +STARTUP_TIME = datetime.now().strftime("%Y-%m-%d_%H-%M") +LOG_FILE = f"timer_logs_{STARTUP_TIME}.csv" + +# Create CSV file with headers if it doesn't exist +if shared_settings.LOG_TIMINGS and not os.path.exists(LOG_FILE): + with open(LOG_FILE, "w", newline="") as f: + writer = csv.writer(f) + writer.writerow(["timestamp", "label", "duration_seconds", "metadata"]) class Timer: + def __init__(self, label="", metadata=None): + self.label = label + self.metadata = metadata or {} + def __enter__(self): self.start_time = time.perf_counter() + self.start_datetime = datetime.now() return self def elapsed_time(self): - return self.start_time - time.perf_counter() + return time.perf_counter() - self.start_time def __exit__(self, exc_type, exc_val, exc_tb): self.end_time = time.perf_counter() self.final_time = self.end_time - self.start_time + + if shared_settings.LOG_TIMINGS: + with open(LOG_FILE, "a", newline="") as f: + writer = csv.writer(f) + writer.writerow([self.start_datetime.isoformat(), self.label, self.final_time, str(self.metadata)]) diff --git a/validator_api/chat_completion.py b/validator_api/chat_completion.py index 144e968a4..8f6b45d22 100644 --- a/validator_api/chat_completion.py +++ b/validator_api/chat_completion.py @@ -89,6 +89,45 @@ async def reconstructed_response() -> AsyncGenerator: return first_chunk, reconstructed_response() +async def stream_chunks( + first_valid_response: AsyncGenerator, + collected_chunks_list: List[List[str]], + timings_list: List[List[float]], + response_start_time: float, +) -> AsyncGenerator[str, None]: + """Stream chunks from a valid response and collect timing data. + + Args: + first_valid_response: The async generator containing response chunks + collected_chunks_list: List to collect response chunks + timings_list: List to collect timing data + response_start_time: Start time of the response for timing calculations + """ + chunks_received = False + async for chunk in first_valid_response: + # Safely handle the chunk + if not chunk.choices or not chunk.choices[0].delta: + continue + + content = getattr(chunk.choices[0].delta, "content", None) + if content is None: + continue + + chunks_received = True + timings_list[0].append(time.monotonic() - response_start_time) + collected_chunks_list[0].append(content) + yield f"data: {json.dumps(chunk.model_dump())}\n\n" + + if not chunks_received: + logger.error("Stream is empty: No chunks were received") + yield 'data: {"error": "502 - Response is empty"}\n\n' + + yield "data: [DONE]\n\n" + + if timings_list and timings_list[0]: + logger.info(f"Response completion time: {timings_list[0][-1]:.2f}s") + + async def stream_from_first_response( responses: List[asyncio.Task], collected_chunks_list: List[List[str]], @@ -129,30 +168,11 @@ async def stream_from_first_response( return # Stream the first valid response - chunks_received = False - async for chunk in first_valid_response: - # Safely handle the chunk - if not chunk.choices or not chunk.choices[0].delta: - continue - - content = getattr(chunk.choices[0].delta, "content", None) - if content is None: - continue - - chunks_received = True - timings_list[0].append(time.monotonic() - response_start_time) - - collected_chunks_list[0].append(content) - yield f"data: {json.dumps(chunk.model_dump())}\n\n" - - if not chunks_received: - logger.error("Stream is empty: No chunks were received") - yield 'data: {"error": "502 - Response is empty"}\n\n' - - yield "data: [DONE]\n\n" + async for chunk_data in stream_chunks( + first_valid_response, collected_chunks_list, timings_list, response_start_time + ): + yield chunk_data - if timings_list and timings_list[0]: - logger.info(f"Response completion time: {timings_list[0][-1]:.2f}s") # Continue collecting remaining responses in background for scoring remaining = asyncio.gather(*pending, return_exceptions=True) remaining_tasks = asyncio.create_task( @@ -298,7 +318,7 @@ async def chat_completion( for task in done: try: response = await task - if response and isinstance(response, tuple): + if response and isinstance(response, tuple) and response[0].choices and response[0].choices[0]: if first_valid_response is None: first_valid_response = response collected_responses.append(response) diff --git a/validator_api/deep_research/orchestrator.py b/validator_api/deep_research/orchestrator.py new file mode 100644 index 000000000..3d399dc70 --- /dev/null +++ b/validator_api/deep_research/orchestrator.py @@ -0,0 +1,576 @@ +import json +from abc import ABC, abstractmethod +from typing import Any + +from loguru import logger +from mistralai import Mistral +from pydantic import BaseModel + +from shared.settings import shared_settings +from validator_api.deep_research.utils import parse_llm_json, with_retries +from validator_api.gpt_endpoints import WebRetrievalRequest, web_retrieval + + +class LLMQuery(BaseModel): + """Records a single LLM API call with its inputs and outputs""" + + messages: list[dict] # The input messages + raw_response: str # The raw response from the LLM + parsed_response: Any | None = None # The parsed response (if applicable) + step_name: str # Name of the step that made this query + timestamp: float # When the query was made + model: str # Which model was used + + +async def search_web(question: str, n_results: int = 5) -> dict: + """ + Takes a natural language question, generates an optimized search query, performs web search, + and returns a referenced answer based on the search results. + + Args: + question: The natural language question to answer + n_results: Number of search results to retrieve + + Returns: + dict containing the answer, references, and search metadata + """ + # Generate optimized search query + query_prompt = """Given a natural language question, generate an optimized web search query. + Focus on extracting key terms and concepts while removing unnecessary words. + Format your response as a single line containing only the optimized search query.""" + + messages = [{"role": "system", "content": query_prompt}, {"role": "user", "content": question}] + + optimized_query, query_record = await make_mistral_request(messages, "optimize_search_query") + + # Perform web search + search_results = await web_retrieval(WebRetrievalRequest(search_query=optimized_query, n_results=n_results)) + + # Generate referenced answer + answer_prompt = f"""Based on the provided search results, generate a comprehensive answer to the question. + Include inline references to sources using markdown format [n] where n is the source number. + + Question: {question} + + Search Results: + {json.dumps([{ + 'index': i + 1, + 'content': result.content, + 'url': result.url + } for i, result in enumerate(search_results.results)], indent=2)} + + Format your response as a JSON object with the following structure: + {{ + "answer": "Your detailed answer with inline references [n]", + "references": [ + {{ + "number": n, + "url": "Source URL" + }} + ] + }}""" + + messages = [ + {"role": "system", "content": answer_prompt}, + {"role": "user", "content": "Please generate a referenced answer based on the search results."}, + ] + + raw_answer, answer_record = await make_mistral_request(messages, "generate_referenced_answer") + answer_data = parse_llm_json(raw_answer) + + return { + "question": question, + "optimized_query": optimized_query, + "answer": answer_data["answer"], + "references": answer_data["references"], + "raw_results": [{"snippet": r.content, "url": r.url} for r in search_results.results], + } + + +@with_retries(max_retries=3) +async def make_mistral_request(messages: list[dict], step_name: str) -> tuple[str, LLMQuery]: + """Makes a request to Mistral API and records the query""" + import time + + model = "mistral-small-latest" + client = Mistral(api_key=shared_settings.GEMMA_API_KEY) + chat_response = client.chat.complete(model=model, messages=messages) + response_content = chat_response.choices[0].message.content + + # Record the query + query_record = LLMQuery( + messages=messages, raw_response=response_content, step_name=step_name, timestamp=time.time(), model=model + ) + + return response_content, query_record + + +class Step(BaseModel): + title: str + content: str + next_step: str | None = None + summary: str | None = None + + def __str__(self): + return f"Title: {self.title}\nContent: {self.content}\nNext Step: {self.next_step}\nSummary: {self.summary}" + + +class StepManager(BaseModel): + steps: list[Step] + + def __str__(self): + output = "Here is the list of steps that were already completed:\n\n" + for i, step in enumerate(self.steps): + output += f"Step {i+1}:\n{step}\n\n" + return output + + +class Tool(ABC): + """Base class for tools that can be used by the orchestrator""" + + @property + @abstractmethod + def name(self) -> str: + """The name of the tool""" + pass + + @property + @abstractmethod + def description(self) -> str: + """Description of what the tool does and how to use it""" + pass + + @abstractmethod + async def execute(self, **kwargs) -> Any: + """Execute the tool with the given parameters""" + pass + + +class WebSearchTool(Tool): + """Tool for performing web searches and getting referenced answers""" + + @property + def name(self) -> str: + return "web_search" + + @property + def description(self) -> str: + return """Searches the web to answer a question. Provides a referenced answer with citations. + Input parameters: + - question: The natural language question to answer + - n_results: (optional) Number of search results to use (default: 5) + + Returns a dictionary containing: + - question: Original question asked + - optimized_query: Search query used + - answer: Detailed answer with inline references [n] + - references: List of numbered references with titles and URLs + - raw_results: Raw search results used""" + + async def execute(self, question: str, n_results: int = 5) -> dict: + return await search_web(question=question, n_results=n_results) + + +class ToolRequest(BaseModel): + """A request to execute a specific tool""" + + tool_name: str + parameters: dict + purpose: str # Why this tool execution is needed for the current step + + +class ToolResult(BaseModel): + """Result of executing a tool""" + + tool_name: str + parameters: dict + result: Any + purpose: str + + +class Orchestrator(BaseModel): + todo_list: str | None = None + current_step: int | None = None + user_messages: str | None = None + max_steps: int = 10 + completed_steps: StepManager = StepManager(steps=[]) + query_history: list[LLMQuery] = [] + tool_history: list[ToolResult] = [] + tools: dict[str, Tool] = {"web_search": WebSearchTool()} + + class Config: + arbitrary_types_allowed = True + + @with_retries(max_retries=3) + async def plan_tool_executions(self) -> list[ToolRequest]: + """Uses mistral LLM to plan which tools to execute for the current step""" + logger.info(f"Planning tool executions for step {self.current_step}") + + tools_description = "\n\n".join([f"Tool: {name}\n{tool.description}" for name, tool in self.tools.items()]) + + prompt = f"""You are planning the use of tools to gather information for the current step in a complex task. + +Available Tools: +{tools_description} + +Current todo list (✓ marks completed steps): +{self.todo_list} + +Previous steps completed: +{self.completed_steps} + +Your task is to determine what tool executions, if any, are needed for the next unchecked step in the todo list. +You can request multiple executions of the same tool with different parameters if needed. + +Format your response as a JSON array of tool requests, where each request has: +- tool_name: Name of the tool to execute +- parameters: Dictionary of parameters for the tool +- purpose: Why this tool execution is needed for the current step + +If no tools are needed, return an empty array. + +Example response: +[ + {{ + "tool_name": "web_search", + "parameters": {{"question": "What are the latest developments in quantum computing?"}}, + "purpose": "To gather recent information about quantum computing advances" + }} +]""" + + messages = [ + {"role": "system", "content": prompt}, + {"role": "user", "content": "Please plan the necessary tool executions for the next step."}, + ] + + plan_output, query_record = await make_mistral_request(messages, f"plan_tools_step_{self.current_step}") + + try: + tool_requests = parse_llm_json(plan_output) + query_record.parsed_response = tool_requests + self.query_history.append(query_record) + + # Validate tool requests + validated_requests = [] + for req in tool_requests: + if req["tool_name"] not in self.tools: + logger.warning(f"Ignoring request for unknown tool: {req['tool_name']}") + continue + validated_requests.append(ToolRequest(**req)) + + if validated_requests: + logger.info(f"Planned {len(validated_requests)} tool executions") + return validated_requests + + except json.JSONDecodeError as e: + logger.error(f"Failed to parse tool planning output as JSON: {e}") + raise + except KeyError as e: + logger.error(f"Missing required key in tool planning output: {e}") + raise + + async def execute_tools(self, tool_requests: list[ToolRequest]) -> list[ToolResult]: + """Executes the requested tools and records their results""" + results = [] + + for request in tool_requests: + logger.info(f"Executing {request.tool_name} - Purpose: {request.purpose}") + tool = self.tools[request.tool_name] + + try: + result = await tool.execute(**request.parameters) + tool_result = ToolResult( + tool_name=request.tool_name, parameters=request.parameters, result=result, purpose=request.purpose + ) + results.append(tool_result) + self.tool_history.append(tool_result) + + except Exception as e: + logger.error(f"Failed to execute {request.tool_name}: {e}") + continue + + return results + + async def run(self, messages): + logger.info("Starting orchestration run") + self.user_messages = messages + await self.generate_todo_list() + + for step in range(self.max_steps): + self.current_step = step + 1 + logger.info(f"Step {step + 1}/{self.max_steps}") + + # Plan and execute tools for this step + tool_requests = await self.plan_tool_executions() + if tool_requests: + await self.execute_tools(tool_requests) + + thinking_result = await self.do_thinking() + + if thinking_result.next_step == "generate_final_answer": + logger.info("Generating final answer") + final_answer = await self.generate_final_answer() + return { + "final_answer": final_answer, + "query_history": self.query_history, + "tool_history": self.tool_history, + } + + await self.update_todo_list() + + final_answer = await self.generate_final_answer() + return {"final_answer": final_answer, "query_history": self.query_history, "tool_history": self.tool_history} + + @with_retries(max_retries=3) + async def generate_todo_list(self): + """Uses mistral LLM to generate a todo list for the Chain of Thought process""" + logger.info("Generating initial todo list") + + prompt = """Based on the conversation history provided, create a focused step-by-step todo list that outlines the thought process needed to find the answer to the user's question. Focus on information gathering, analysis, and validation steps. + +Key principles: +1. Break down the problem into clear analytical steps +2. Focus on what information needs to be gathered and analyzed +3. Include validation steps to verify findings +4. Consider what tools might be needed at each step +5. DO NOT include report writing or summarization in the steps - that will be handled in the final answer + +Format your response as a numbered list where each item follows this structure: +1. [Analysis/Research Task]: What needs to be investigated or analyzed + - Information needed: What specific data or insights we need to gather + - Approach: How we'll gather this information (e.g., which tools might help) + - Validation: How we'll verify the information is accurate and complete + +Your todo list should focus purely on the steps needed to find and validate the answer, not on presenting it. +""" + + messages = [ + {"role": "system", "content": prompt}, + { + "role": "user", + "content": f"Here is the conversation history to base the todo list on:\n{self.user_messages}", + }, + ] + + response, query_record = await make_mistral_request(messages, "generate_todo_list") + self.query_history.append(query_record) + self.todo_list = response + return self.todo_list + + @with_retries(max_retries=3) + async def do_thinking(self) -> Step: + """Uses mistral LLM to generate thinking/reasoning tokens in line with the todo list""" + logger.info(f"Analyzing step {self.current_step}") + + prompt = f"""You are a systematic problem solver working through a complex task step by step. You have a todo list to follow, and you're currently on step {self.current_step}. Your goal is to think deeply about this step and provide clear, logical reasoning. + +Here is your todo list (✓ marks completed steps): +{self.todo_list} + +Find the first unchecked item in the todo list (items without a ✓) and analyze that step. Provide your response in the following JSON format: +{{ + "thinking_step_title": "Title of the current todo list step being analyzed", + "thoughts": "Your detailed analysis and reasoning about this step, including: + - Step-by-step reasoning process + - Consideration of edge cases and potential issues + - References to previous steps if relevant + - Validation of your approach + - Summary of the process that clearly states the answer to the todo list step", + "summary": "A concise summary of your conclusions and key takeaways from this step", + "next_action": "Either 'continue_thinking' if there are more unchecked todo steps to process, or 'generate_final_answer' if all steps are checked" +}}""" + + messages = [ + {"role": "system", "content": prompt}, + { + "role": "user", + "content": f"Here is the conversation history to base your thinking on:\n{self.user_messages}", + }, + ] + + thinking_output, query_record = await make_mistral_request(messages, f"thinking_step_{self.current_step}") + + try: + thinking_dict = parse_llm_json(thinking_output) + query_record.parsed_response = thinking_dict + self.query_history.append(query_record) + + step = Step( + title=thinking_dict["thinking_step_title"], + content=thinking_dict["thoughts"], + next_step=thinking_dict["next_action"], + summary=thinking_dict["summary"], + ) + logger.info(f"Completed analysis: {step.title}") + self.completed_steps.steps.append(step) + return step + except json.JSONDecodeError as e: + logger.error(f"Failed to parse thinking output as JSON: {e}") + raise + except KeyError as e: + logger.error(f"Missing required key in thinking output: {e}") + raise + + @with_retries(max_retries=3) + async def update_todo_list(self): + """Uses mistral LLM to update the todo list based on the steps taken""" + logger.info("Updating todo list") + + prompt = f"""You are responsible for reviewing and updating the todo list based on the latest thinking step. + +Current todo list: +{self.todo_list} + +Latest completed thinking step: +{self.completed_steps.steps[-1]} + +Previous completed steps: +{self.completed_steps.steps[:-1]} + +Your task is to: +1. Review the current todo list and completed steps +2. Mark completed items with a checkmark (✓) at the start of the line +3. Determine if any new tasks have emerged from the latest analysis +4. Assess if any existing tasks need to be modified based on new insights +5. Check if any tasks are now redundant or can be removed +6. Ensure task dependencies are still accurate + +When marking items as complete: +- Add a "✓ " at the start of any numbered item that has been fully addressed in the completed steps +- The checkmark should be added before the number, like this: "✓ 1. [Task Name]" +- If a task was partially completed, do not add a checkmark +- Keep the original numbering intact, just add the checkmark before the number +- Maintain any existing checkmarks from previous updates + +Format your response in the following JSON structure: +{{ + "updated_todo_list": "The complete, updated todo list with checkmarks for completed items", + "changes_made": [ + "list of specific changes made to the todo list and why" + ], + "next_step_number": number, + "rationale": "Brief explanation of why these updates were necessary" +}}""" + + messages = [ + {"role": "system", "content": prompt}, + {"role": "user", "content": f"Here is the conversation history for context:\n{self.user_messages}"}, + ] + + updated_todo, query_record = await make_mistral_request(messages, f"update_todo_list_step_{self.current_step}") + + try: + updated_todo_dict = parse_llm_json(updated_todo) + query_record.parsed_response = updated_todo_dict + self.query_history.append(query_record) + + self.todo_list = updated_todo_dict["updated_todo_list"] + logger.info(f"Updated todo list with {len(updated_todo_dict['changes_made'])} changes") + return updated_todo_dict + except json.JSONDecodeError as e: + logger.error(f"Failed to parse updated todo list as JSON: {e}") + raise + except KeyError as e: + logger.error(f"Missing required key in updated todo list: {e}") + raise + + @with_retries(max_retries=3) + async def generate_final_answer(self): + """Uses mistral LLM to generate a final answer to the user's request""" + logger.info("Generating final answer") + logger.debug(f"Completed steps for final answer:\n{self.completed_steps}") + + prompt = f"""You are tasked with providing a clear, direct answer to the user's original question based on the analysis performed. Your goal is to synthesize all the information gathered into a helpful response. + +Original user question: +{self.user_messages} + +Analysis performed: +TODO list (✓ marks completed steps): +{self.todo_list} + +Completed thinking steps: +{self.completed_steps} + +Tool execution history: +{json.dumps([{ + 'tool': result.tool_name, + 'purpose': result.purpose, + 'result': result.result +} for result in self.tool_history], indent=2)} + +Your task is to: +1. Review all the information gathered +2. Synthesize the findings into a clear answer +3. Directly address the user's original question +4. Include relevant supporting evidence and citations +5. Acknowledge any limitations or uncertainties + +Format your response as a JSON object with the following structure: +{{ + "direct_answer": "A clear, concise answer to the user's question", + "detailed_explanation": "A more detailed explanation with supporting evidence and reasoning", + "sources_and_evidence": [ + {{ + "point": "Key point or claim made", + "evidence": "Evidence supporting this point", + "source": "Where this information came from (if applicable)" + }} + ], + "limitations": [ + "Any limitations, caveats, or uncertainties in the answer" + ] +}} + +Focus on providing a helpful, accurate answer to what the user actually asked.""" + + messages = [ + {"role": "system", "content": prompt}, + {"role": "user", "content": "Please generate a final answer based on the analysis performed."}, + ] + + final_answer, query_record = await make_mistral_request(messages, "generate_final_answer") + logger.debug(f"Generated final answer:\n{final_answer}") + + try: + final_answer_dict = parse_llm_json(final_answer) + query_record.parsed_response = final_answer_dict + self.query_history.append(query_record) + + return final_answer_dict + except json.JSONDecodeError as e: + logger.error(f"Failed to parse final answer as JSON: {e}") + raise + except KeyError as e: + logger.error(f"Missing required key in final answer: {e}") + raise + + +# def make_gemma_request(messages): +# """Makes a request to the gemma LLM""" +# import requests +# import os +# import json + +# url = "https://generativelanguage.googleapis.com/v1beta/models/gemma-3-27b-it:generateContent" +# headers = { +# "Content-Type": "application/json" +# } + +# # Get API key from environment +# # Construct request payload +# payload = { +# "contents": [{ +# "parts": [{"text": message["content"]} for message in messages] +# }] +# } + +# # Make request +# response = requests.post( +# f"{url}?key={shared_settings.GEMMA_API_KEY}", +# headers=headers, +# json=payload +# ) + +# output = response.json() +# return output["candidates"][0]["content"]["parts"][0]["text"] diff --git a/validator_api/deep_research/orchestrator_v2.py b/validator_api/deep_research/orchestrator_v2.py new file mode 100644 index 000000000..395dc07eb --- /dev/null +++ b/validator_api/deep_research/orchestrator_v2.py @@ -0,0 +1,721 @@ +import asyncio +import json +import time +from abc import ABC, abstractmethod +from datetime import datetime +from typing import Any, Awaitable, Callable, Optional + +from fastapi.responses import StreamingResponse +from loguru import logger +from pydantic import BaseModel +from tenacity import retry, retry_if_exception_type, stop_after_attempt, wait_exponential + +from validator_api.deep_research.utils import parse_llm_json, with_retries +from validator_api.serializers import CompletionsRequest, WebRetrievalRequest +from validator_api.web_retrieval import web_retrieval + + +def make_chunk(text): + chunk = json.dumps({"choices": [{"delta": {"content": text}}]}) + return f"data: {chunk}\n\n" + + +def get_current_datetime_str() -> str: + """Returns a nicely formatted string of the current date and time""" + return datetime.now().strftime("%B %d, %Y") + + +class LLMQuery(BaseModel): + """Records a single LLM API call with its inputs and outputs""" + + messages: list[dict] # The input messages + raw_response: str # The raw response from the LLM + parsed_response: Any | None = None # The parsed response (if applicable) + step_name: str # Name of the step that made this query + timestamp: float # When the query was made + model: str # Which model was used + + +async def search_web(question: str, n_results: int = 5, completions=None) -> dict: + """ + Takes a natural language question, generates an optimized search query, performs web search, + and returns a referenced answer based on the search results. + + Args: + question: The natural language question to answer + n_results: Number of search results to retrieve + completions: Function to make completions request + + Returns: + dict containing the answer, references, and search metadata + """ + # Generate optimized search query + query_prompt = """Given a natural language question, generate an optimized web search query. + Focus on extracting key terms and concepts while removing unnecessary words. + Format your response as a single line containing only the optimized search query.""" + + messages = [{"role": "system", "content": query_prompt}, {"role": "user", "content": question}] + + optimized_query, query_record = await make_mistral_request( + messages, "optimize_search_query", completions=completions + ) + + # Perform web search + search_results = await web_retrieval(WebRetrievalRequest(search_query=optimized_query, n_results=n_results)) + + # Generate referenced answer + answer_prompt = f"""Based on the provided search results, generate a comprehensive answer to the question. + Include inline references to sources using markdown format [n] where n is the source number. + + Question: {question} + + Search Results: + {json.dumps([{ + 'index': i + 1, + 'content': result.content, + 'url': result.url + } for i, result in enumerate(search_results.results)], indent=2)} + + Format your response as a JSON object with the following structure: + {{ + "answer": "Your detailed answer with inline references [n]", + "references": [ + {{ + "number": n, + "url": "Source URL" + }} + ] + }}""" + + messages = [ + {"role": "system", "content": answer_prompt}, + {"role": "user", "content": "Please generate a referenced answer based on the search results."}, + ] + + raw_answer, answer_record = await make_mistral_request( + messages, "generate_referenced_answer", completions=completions + ) + answer_data = parse_llm_json(raw_answer) + + return { + "question": question, + "optimized_query": optimized_query, + "answer": answer_data["answer"], + "references": answer_data["references"], + "raw_results": [{"snippet": r.content, "url": r.url} for r in search_results.results], + } + + +@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=2, max=15), retry=retry_if_exception_type()) +async def make_mistral_request( + messages: list[dict], step_name: str, completions: Callable[[CompletionsRequest], Awaitable[StreamingResponse]] +) -> tuple[str, LLMQuery]: + """Makes a request to Mistral API and records the query""" + + model = "mrfakename/mistral-small-3.1-24b-instruct-2503-hf" + temperature = 0.1 + top_p = 1 + max_tokens = 128000 + sample_params = { + "top_p": top_p, + "max_tokens": max_tokens, + "temperature": temperature, + "do_sample": False, + } + logger.info(f"Making request to Mistral API with model: {model}") + response = await completions( + CompletionsRequest(messages=messages, model=model, stream=False, sampling_parameters=sample_params) + ) + response_content = response.choices[0].message.content + # Record the query + query_record = LLMQuery( + messages=messages, raw_response=response_content, step_name=step_name, timestamp=time.time(), model=model + ) + + return response_content, query_record + + +class Step(BaseModel): + title: str + content: str + next_step: str | None = None + summary: str | None = None + + def __str__(self): + return f"Title: {self.title}\nContent: {self.content}\nNext Step: {self.next_step}\nSummary: {self.summary}" + + +class StepManager(BaseModel): + steps: list[Step] + + def __str__(self): + output = "Here is the list of steps that were already completed:\n\n" + for i, step in enumerate(self.steps): + output += f"Step {i+1}:\n{step}\n\n" + return output + + +class Tool(ABC): + """Base class for tools that can be used by the orchestrator""" + + @property + @abstractmethod + def name(self) -> str: + """The name of the tool""" + pass + + @property + @abstractmethod + def description(self) -> str: + """Description of what the tool does and how to use it""" + pass + + @abstractmethod + async def execute(self, **kwargs) -> Any: + """Execute the tool with the given parameters""" + pass + + +class WebSearchTool(Tool): + """Tool for performing web searches and getting referenced answers""" + + def __init__(self, completions=None): + self.completions = completions + + @property + def name(self) -> str: + return "web_search" + + @property + def description(self) -> str: + return """Searches the web to answer a question. Provides a referenced answer with citations. + Input parameters: + - question: The natural language question to answer + - n_results: (optional) Number of search results to use (default: 5) + + Returns a dictionary containing: + - question: Original question asked + - optimized_query: Search query used + - answer: Detailed answer with inline references [n] + - references: List of numbered references with titles and URLs + - raw_results: Raw search results used""" + + async def execute(self, question: str, n_results: int = 5) -> dict: + return await search_web(question=question, n_results=n_results, completions=self.completions) + + +class ToolRequest(BaseModel): + """A request to execute a specific tool""" + + tool_name: str + parameters: dict + purpose: str # Why this tool execution is needed for the current step + + +class ToolResult(BaseModel): + """Result of executing a tool""" + + tool_name: str + parameters: dict + result: Any + purpose: str + + +class OrchestratorV2(BaseModel): + todo_list: str | None = None + current_step: int | None = None + user_messages: str | None = None + max_steps: int = 10 + completed_steps: StepManager = StepManager(steps=[]) + query_history: list[LLMQuery] = [] + tool_history: list[ToolResult] = [] + completions: Optional[Callable[[CompletionsRequest], Awaitable[StreamingResponse]]] = None + tools: dict[str, Tool] = {} + + class Config: + arbitrary_types_allowed = True + + def __init__(self, **data): + super().__init__(**data) + # Initialize tools with the completions function + self.tools = {"web_search": WebSearchTool(completions=self.completions)} + + async def assess_question_suitability( + self, question: str, completions: Callable[[CompletionsRequest], Awaitable[StreamingResponse]] = None + ) -> dict: + logger.info(f"assess_question_suitability: {question}") + logger.info(f"completions: {completions}") + logger.info(f"self.completions: {self.completions}") + + """ + Assesses whether a question is suitable for deep research or if it can be answered directly. + + Args: + question: The user's question to assess + + Returns: + dict containing assessment results with: + - is_suitable: Boolean indicating if deep research is needed + - reason: Explanation of the assessment + - direct_answer: Simple answer if question doesn't need deep research + """ + + assessment_prompt = f"""You are part of Apex, a Deep Research Assistant. Your purpose is to assess whether a question is suitable for deep research or if it can be answered directly. The current date and time is {get_current_datetime_str()}. + + Task: + Evaluate the given question and determine if it: + + 1. Requires deep research (complex topics, factual research, analysis of multiple sources, or needs verification through web search) + 2. Can be answered directly (simple questions, greetings, opinions, or well-known facts that do not require research) + + # Definitions + ## Deep research questions typically: + - Seek factual information that may require up-to-date or verified data (e.g., prices, event times, current status) + - Involve complex topics with nuance, such as technical processes, system design, or multi-step methodologies + - Request detailed breakdowns, plans, or analysis grounded in domain-specific knowledge (e.g., engineering, AI development) + - Require synthesis of information from multiple or external sources + - Involve comparing different perspectives, approaches, or technologies + - Would reasonably benefit from web search, expert resources, or tool use to provide a comprehensive answer + + ## Questions NOT suitable for deep research include: + - Simple greetings or conversational remarks (e.g., "How are you?", "Hello") + - Basic opinions that don't require factual grounding or research + - Simple, well-known facts that don't need verification (e.g., "The sky is blue") + - Requests for purely imaginative content like poems, stories, or fictional narratives + - Personal questions about the AI assistant (e.g., "What's your favorite color?") + - Questions with obvious or unambiguous answers that don't benefit from external tools or elaboration + + Response Format: + Format your response as a JSON object with the following structure: + {{ + "is_suitable": boolean, // true if deep research or a web search is needed, false if not + "reason": "Brief explanation of why the question does or doesn't need deep research", + "direct_answer": "If the question doesn't need deep research, provide a direct answer here. Otherwise, null." + }} + """ + + messages = [ + {"role": "system", "content": assessment_prompt}, + {"role": "user", "content": question}, + ] + + assessment_result, query_record = await make_mistral_request( + messages, "assess_question_suitability", completions=self.completions + ) + + try: + assessment_data = parse_llm_json(assessment_result) + query_record.parsed_response = assessment_data + return assessment_data + except json.JSONDecodeError as e: + logger.error(f"Failed to parse question assessment output as JSON: {e}") + return { + "is_suitable": True, + "reason": "Unable to assess question suitability due to parsing error. Proceeding with deep research.", + "direct_answer": None, + } + + @with_retries(max_retries=3) + async def plan_tool_executions(self) -> list[ToolRequest]: + """Uses mistral LLM to plan which tools to execute for the current step""" + logger.info(f"Planning tool executions for step {self.current_step}") + + tools_description = "\n\n".join([f"Tool: {name}\n{tool.description}" for name, tool in self.tools.items()]) + + prompt = f"""You are planning the use of tools to gather information for the current step in a complex task. The current date and time is {get_current_datetime_str()}. + + Available Tools: + {tools_description} + + Current todo list (✓ marks completed steps): + {self.todo_list} + + Previous steps completed: + {self.completed_steps} + + Your task is to determine what tool executions, if any, are needed for the next unchecked step in the todo list. + You can request multiple executions of the same tool with different parameters if needed. + + Format your response as a JSON array of tool requests, where each request has: + - tool_name: Name of the tool to execute + - parameters: Dictionary of parameters for the tool + - purpose: Why this tool execution is needed for the current step + + If no tools are needed, return an empty array. + + Example response: + [ + {{ + "tool_name": "web_search", + "parameters": {{"question": "What are the latest developments in quantum computing?"}}, + "purpose": "To gather recent information about quantum computing advances" + }} + ] + """ + + messages = [ + {"role": "system", "content": prompt}, + {"role": "user", "content": "Please plan the necessary tool executions for the next step."}, + ] + + plan_output, query_record = await make_mistral_request( + messages, f"plan_tools_step_{self.current_step}", completions=self.completions + ) + + try: + tool_requests = parse_llm_json(plan_output) + query_record.parsed_response = tool_requests + self.query_history.append(query_record) + + # Validate tool requests + validated_requests = [] + for req in tool_requests: + if req["tool_name"] not in self.tools: + logger.warning(f"Ignoring request for unknown tool: {req['tool_name']}") + continue + validated_requests.append(ToolRequest(**req)) + + if validated_requests: + logger.info(f"Planned {len(validated_requests)} tool executions") + return validated_requests + + except json.JSONDecodeError as e: + logger.error(f"Failed to parse tool planning output as JSON: {e}") + raise + except KeyError as e: + logger.error(f"Missing required key in tool planning output: {e}") + raise + + async def execute_tools(self, tool_requests: list[ToolRequest]) -> list[ToolResult]: + """Executes the requested tools concurrently and records their results""" + + async def execute_single_tool(request: ToolRequest) -> ToolResult | None: + """Helper function to execute a single tool and handle exceptions""" + logger.info(f"Executing {request.tool_name} - Purpose: {request.purpose}") + tool = self.tools[request.tool_name] + + try: + result = await tool.execute(**request.parameters) + return ToolResult( + tool_name=request.tool_name, parameters=request.parameters, result=result, purpose=request.purpose + ) + except Exception as e: + logger.error(f"Failed to execute {request.tool_name}: {e}") + return None + + # Execute all tool requests concurrently + tool_results = await asyncio.gather(*[execute_single_tool(request) for request in tool_requests]) + + # Filter out None results (from failed executions) and record successful results + results = [result for result in tool_results if result is not None] + self.tool_history.extend(results) + + return results + + async def run(self, messages): + logger.info("Starting orchestration run") + self.user_messages = messages + + # Always take the last user message as the question + question = messages[-1]["content"] + logger.info(f"self.completions: {self.completions}") + # First assess if the question is suitable for deep research + question_assessment = await self.assess_question_suitability(question, self.completions) + + # If the question is not suitable for deep research, return a direct answer + if not question_assessment["is_suitable"]: + logger.info(f"Question not suitable for deep research: {question_assessment['reason']}") + yield make_chunk(question_assessment["direct_answer"]) + yield "data: [DONE]\n\n" + return + + # Continue with deep research process + yield make_chunk("## Generating Research Plan\n") + await self.generate_todo_list() + yield make_chunk(f"## Research Plan\n{self.todo_list}\n") + + for step in range(self.max_steps): + self.current_step = step + 1 + logger.info(f"Step {step + 1}/{self.max_steps}") + + # Plan and execute tools for this step + yield make_chunk(f"\n## Step {step + 1}: Planning Tools\n") + tool_requests = await self.plan_tool_executions() + + if tool_requests: + for request in tool_requests: + yield make_chunk(f"\n## Executing {request.tool_name}\n{request.purpose}\n") + results = await self.execute_tools(tool_requests) + for result in results: + yield make_chunk(f"\n### Tool Results\n{result.tool_name} execution complete\n") + + yield make_chunk(f"\n## Analyzing Step {step + 1}\n") + thinking_result = await self.do_thinking() + yield make_chunk(f"\n## Step {step + 1} Summary\n{thinking_result.summary}\n") + + if thinking_result.next_step == "generate_final_answer": + logger.info("Generating final answer") + yield make_chunk("\n## Generating Final Answer\n") + final_answer = await self.generate_final_answer() + yield make_chunk(f"\n## Final Answer\n{final_answer}\n") + return + + yield make_chunk("\n## Updating Research Plan\n") + await self.update_todo_list() + yield make_chunk("\n## Research Plan Updated\n") + + yield make_chunk("\n## Generating Final Answer\n") + final_answer = await self.generate_final_answer() + yield make_chunk(f"\n# Final Answer\n{final_answer}\n") + yield "data: [DONE]\n\n" + + @with_retries(max_retries=3) + async def generate_todo_list(self): + """Uses mistral LLM to generate a todo list for the Chain of Thought process""" + logger.info("Generating initial todo list") + + prompt = """Based on the conversation history provided, create a focused step-by-step todo list that outlines the thought process needed to find the answer to the user's question. Focus on information gathering, analysis, and validation steps. + + Key principles: + 1. Break down the problem into clear analytical steps + 2. Focus on what information needs to be gathered and analyzed + 3. Include validation steps to verify findings + 4. Consider what tools might be needed at each step + 5. DO NOT include report writing or summarization in the steps - that will be handled in the final answer + + Format your response as a numbered list where each item follows this structure: + 1. [Analysis/Research Task]: What needs to be investigated or analyzed + - Information needed: What specific data or insights we need to gather + - Approach: How we'll gather this information (e.g., which tools might help) + - Validation: How we'll verify the information is accurate and complete + + Your todo list should focus purely on the steps needed to find and validate the answer, not on presenting it. + """ + + messages = [ + {"role": "system", "content": prompt}, + { + "role": "user", + "content": f"Here is the conversation history to base the todo list on:\n{self.user_messages}", + }, + ] + + response, query_record = await make_mistral_request( + messages, "generate_todo_list", completions=self.completions + ) + self.query_history.append(query_record) + self.todo_list = response + return self.todo_list + + @with_retries(max_retries=3) + async def do_thinking(self) -> Step: + """Uses mistral LLM to generate thinking/reasoning tokens in line with the todo list""" + logger.info(f"Analyzing step {self.current_step}") + + prompt = f""" + You are a systematic problem solver working through a complex task step by step. The current date and time is {get_current_datetime_str()}. You have a todo list to follow, and you're currently on step {self.current_step}. Your goal is to think deeply about this step and provide clear, logical reasoning. + +Here is your todo list (✓ marks completed steps): +{self.todo_list} + +Find the first unchecked item in the todo list (items without a ✓) and analyze that step. Provide your response in the following JSON format: +{{ + "thinking_step_title": "Title of the current todo list step being analyzed", + "thoughts": "Your detailed analysis and reasoning about this step, including: + - Step-by-step reasoning process + - Consideration of edge cases and potential issues + - References to previous steps if relevant + - Validation of your approach + - Summary of the process that clearly states the answer to the todo list step", + "summary": "A concise summary of your conclusions and key takeaways from this step", + "next_action": "Either 'continue_thinking' if there are more unchecked todo steps to process, or 'generate_final_answer' if all steps are checked" +}}""" + + messages = [ + {"role": "system", "content": prompt}, + { + "role": "user", + "content": f"Here is the conversation history to base your thinking on:\n{self.user_messages}", + }, + ] + + thinking_output, query_record = await make_mistral_request( + messages, f"thinking_step_{self.current_step}", completions=self.completions + ) + + try: + thinking_dict = parse_llm_json(thinking_output) + query_record.parsed_response = thinking_dict + self.query_history.append(query_record) + + step = Step( + title=thinking_dict["thinking_step_title"], + content=thinking_dict["thoughts"], + next_step=thinking_dict["next_action"], + summary=thinking_dict["summary"], + ) + logger.info(f"Completed analysis: {step.title}") + self.completed_steps.steps.append(step) + return step + except json.JSONDecodeError as e: + logger.error(f"Failed to parse thinking output as JSON: {e}") + raise + except KeyError as e: + logger.error(f"Missing required key in thinking output: {e}") + raise + + @with_retries(max_retries=3) + async def update_todo_list(self): + """Uses mistral LLM to update the todo list based on the steps taken""" + logger.info("Updating todo list") + + prompt = f"""You are responsible for reviewing and updating the todo list based on the latest thinking step. + +Current todo list: +{self.todo_list} + +Latest completed thinking step: +{self.completed_steps.steps[-1]} + +Previous completed steps: +{self.completed_steps.steps[:-1]} + +Your task is to: +1. Review the current todo list and completed steps +2. Mark completed items with a checkmark (✓) at the start of the line +3. Determine if any new tasks have emerged from the latest analysis +4. Assess if any existing tasks need to be modified based on new insights +5. Check if any tasks are now redundant or can be removed +6. Ensure task dependencies are still accurate + +When marking items as complete: +- Add a "✓ " at the start of any numbered item that has been fully addressed in the completed steps +- The checkmark should be added before the number, like this: "✓ 1. [Task Name]" +- If a task was partially completed, do not add a checkmark +- Keep the original numbering intact, just add the checkmark before the number +- Maintain any existing checkmarks from previous updates + +Format your response in the following JSON structure: +{{ + "updated_todo_list": "The complete, updated todo list with checkmarks for completed items", + "changes_made": [ + "list of specific changes made to the todo list and why" + ], + "next_step_number": number, + "rationale": "Brief explanation of why these updates were necessary" +}}""" + + messages = [ + {"role": "system", "content": prompt}, + {"role": "user", "content": f"Here is the conversation history for context:\n{self.user_messages}"}, + ] + + updated_todo, query_record = await make_mistral_request( + messages, f"update_todo_list_step_{self.current_step}", completions=self.completions + ) + + try: + updated_todo_dict = parse_llm_json(updated_todo) + query_record.parsed_response = updated_todo_dict + self.query_history.append(query_record) + + self.todo_list = updated_todo_dict["updated_todo_list"] + logger.info(f"Updated todo list with {len(updated_todo_dict['changes_made'])} changes") + return updated_todo_dict + except json.JSONDecodeError as e: + logger.error(f"Failed to parse updated todo list as JSON: {e}") + raise + except KeyError as e: + logger.error(f"Missing required key in updated todo list: {e}") + raise + + @with_retries(max_retries=3) + async def generate_final_answer(self): + """Uses mistral LLM to generate a final answer to the user's request""" + logger.info("Generating final answer") + logger.debug(f"Completed steps for final answer:\n{self.completed_steps}") + + prompt = f"""You are tasked with providing a clear, direct answer to the user's original question based on the analysis performed. The current date and time is {get_current_datetime_str()}. Your goal is to synthesize all the information gathered into a helpful response. + +Original user question: +{self.user_messages} + +Analysis performed: +TODO list (✓ marks completed steps): +{self.todo_list} + +Completed thinking steps: +{self.completed_steps} + +Tool execution history: +{json.dumps([{ + 'tool': result.tool_name, + 'purpose': result.purpose, + 'result': result.result +} for result in self.tool_history], indent=2)} + +Your task is to: +1. Review all the information gathered +2. Synthesize the findings into a clear answer +3. Directly address the user's original question +4. Include relevant supporting evidence and citations +5. Acknowledge any limitations or uncertainties + +Format your response as a JSON object with the following structure: +{{ + "direct_answer": "A clear, concise answer to the user's question", + "detailed_explanation": "A more detailed explanation with supporting evidence and reasoning", + "sources_and_evidence": [ + {{ + "point": "Key point or claim made", + "evidence": "Evidence supporting this point", + "source": "Where this information came from (if applicable)" + }} + ], + "limitations": [ + "Any limitations, caveats, or uncertainties in the answer" + ] +}} + +Focus on providing a helpful, accurate answer to what the user actually asked.""" + + messages = [ + {"role": "system", "content": prompt}, + {"role": "user", "content": "Please generate a final answer based on the analysis performed."}, + ] + + final_answer, query_record = await make_mistral_request( + messages, "generate_final_answer", completions=self.completions + ) + logger.debug(f"Generated final answer:\n{final_answer}") + + try: + final_answer_dict = parse_llm_json(final_answer) + query_record.parsed_response = final_answer_dict + self.query_history.append(query_record) + + return final_answer_dict + except json.JSONDecodeError as e: + logger.error(f"Failed to parse final answer as JSON: {e}") + raise + except KeyError as e: + logger.error(f"Missing required key in final answer: {e}") + raise + + +if __name__ == "__main__": + + async def main(): + orchestrator = OrchestratorV2() + try: + # We would need a real completions function here, but since this is just an example, + # we'll use None and it will fail gracefully + async for chunk in orchestrator.run( + messages=[{"role": "user", "content": "How can I implement a prompt engineering project?"}], + completions=None, + ): + print(chunk) + except Exception as e: + print(f"An error occurred: {e}") + + asyncio.run(main()) diff --git a/validator_api/deep_research/persistent_cache.py b/validator_api/deep_research/persistent_cache.py new file mode 100644 index 000000000..243699fa9 --- /dev/null +++ b/validator_api/deep_research/persistent_cache.py @@ -0,0 +1,94 @@ +import functools +import hashlib +import inspect +import json +import os + + +def persistent_cache(cache_file=None): + """ + Decorator that creates a persistent cache for function calls. + + Args: + cache_file (str, optional): Path to the cache file. If None, uses the function name. + + Returns: + function: Decorated function with persistent caching. + """ + + def decorator(func): + # Get the file path for the cache + if cache_file is None: + # Default to function name in current working directory if module path not available + try: + module_path = inspect.getmodule(func).__file__ + module_dir = os.path.dirname(os.path.abspath(module_path)) + except AttributeError: + module_dir = os.getcwd() + cache_path = os.path.join(module_dir, f"{func.__name__}_cache.json") + else: + cache_path = cache_file + + # Load existing cache if it exists + if os.path.exists(cache_path): + try: + with open(cache_path, "r") as f: + cache = json.load(f) + except (json.JSONDecodeError, IOError): + cache = {} + else: + cache = {} + + @functools.wraps(func) + def wrapper(*args, **kwargs): + # Create a hash of the arguments to use as a cache key + # We need to handle non-hashable arguments (like lists and dicts) + key_parts = [] + + # Add function name to ensure different functions don't share cache keys + key_parts.append(func.__name__) + + # Process positional arguments + for arg in args: + if isinstance(arg, (list, dict, set)): + # Convert to a string representation for hashing + key_parts.append(hashlib.md5(json.dumps(arg, sort_keys=True).encode()).hexdigest()) + else: + key_parts.append(str(arg)) + + # Process keyword arguments (sorted for consistency) + for k in sorted(kwargs.keys()): + v = kwargs[k] + if isinstance(v, (list, dict, set)): + key_parts.append(f"{k}={hashlib.md5(json.dumps(v, sort_keys=True).encode()).hexdigest()}") + else: + key_parts.append(f"{k}={v}") + + # Create the final cache key + cache_key = hashlib.md5("|".join(key_parts).encode()).hexdigest() + + # Check if result is in cache + if cache_key in cache: + print(f"Cache hit for {func.__name__}! Returning cached result.") + return cache[cache_key] + + # If not in cache, call the function and store the result + result = func(*args, **kwargs) + + # Try to make result JSON serializable + try: + # Test if the result is JSON serializable + json.dumps(result) + cache[cache_key] = result + + # Save the updated cache + with open(cache_path, "w") as f: + json.dump(cache, f, indent=2) + except (TypeError, OverflowError): + print(f"Warning: Result from {func.__name__} is not JSON serializable. Not caching.") + + return result + + return wrapper + + return decorator diff --git a/validator_api/deep_research/test.ipynb b/validator_api/deep_research/test.ipynb new file mode 100644 index 000000000..1da424f81 --- /dev/null +++ b/validator_api/deep_research/test.ipynb @@ -0,0 +1,1387 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:34:53.506\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mshared.settings\u001b[0m:\u001b[36mvalidate_mode\u001b[0m:\u001b[36m189\u001b[0m - \u001b[33m\u001b[1mNo .env.validator file found. Please create one.\u001b[0m\n", + "\u001b[32m2025-03-24 14:34:53.509\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mshared.settings\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m287\u001b[0m - \u001b[1mShared settings loaded.\u001b[0m\n", + "\u001b[32m2025-03-24 14:34:54.074\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mshared.settings\u001b[0m:\u001b[36mMETAGRAPH\u001b[0m:\u001b[36m265\u001b[0m - \u001b[1mInstantiating metagraph with NETUID: 61\u001b[0m\n", + "\u001b[32m2025-03-24 14:34:54.076\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mshared.settings\u001b[0m:\u001b[36mSUBTENSOR\u001b[0m:\u001b[36m260\u001b[0m - \u001b[1mInstantiating subtensor with network: test\u001b[0m\n", + "\u001b[32m2025-03-24 14:34:56.430\u001b[0m | \u001b[31m\u001b[1mERROR \u001b[0m | \u001b[36mvalidator_api.api_management\u001b[0m:\u001b[36mload_api_keys\u001b[0m:\u001b[36m20\u001b[0m - \u001b[31m\u001b[1mAPI keys are not found: api_keys.json\u001b[0m\n", + "\u001b[32m2025-03-24 14:34:56.459\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mprompting\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m18\u001b[0m - \u001b[1mProject version: 2.17.4\u001b[0m\n" + ] + } + ], + "source": [ + "from validator_api.deep_research.utils import convert_to_gemma_messages\n", + "from validator_api.deep_research.orchestrator_v2 import OrchestratorV2, make_mistral_request, shared_settings" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:34:56.689\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mrun\u001b[0m:\u001b[36m373\u001b[0m - \u001b[1mStarting orchestration run\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:34:58.266\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mgenerate_todo_list\u001b[0m:\u001b[36m514\u001b[0m - \u001b[1mGenerating initial todo list\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '# Generating Research Plan\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:35:02.737\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mrun\u001b[0m:\u001b[36m413\u001b[0m - \u001b[1mStep 1/10\u001b[0m\n", + "\u001b[32m2025-03-24 14:35:02.738\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mplan_tool_executions\u001b[0m:\u001b[36m284\u001b[0m - \u001b[1mPlanning tool executions for step 1\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': \"## Research Plan\\n1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid.\\n - Information needed: The formula for the number of glasses in each layer of a square pyramid.\\n - Approach: Use the formula for the nth triangular number, since each layer of a square pyramid is a square number of glasses. The formula for the nth triangular number is T_n = n(n + 1)/2. Since we need square numbers, we use n^2 for each layer.\\n - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results.\\n\\n2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have.\\n - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom.\\n - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50.\\n - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom.\\n\\n3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers.\\n - Information needed: The total number of glasses in a pyramid with 50 layers.\\n - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6.\\n - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results.\\n\\n4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass.\\n - Information needed: The average volume of a champagne glass.\\n - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation.\\n - Validation: Confirm the volume by checking multiple sources and calculating an average.\\n\\n5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass.\\n - Information needed: The total volume of champagne required to fill all the glasses.\\n - Approach: Multiply the total number of glasses by the average volume of a champagne glass.\\n - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent.\\n\\n6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles).\\n - Information needed: The conversion factor from the calculated volume unit to liters or bottles.\\n - Approach: Use standard conversion factors to convert the total volume to liters or bottles.\\n - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary.\\n\"}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Step 1: Planning Tools\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:35:04.386\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mplan_tool_executions\u001b[0m:\u001b[36m340\u001b[0m - \u001b[1mPlanned 1 tool executions\u001b[0m\n", + "\u001b[32m2025-03-24 14:35:04.387\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mexecute_tools\u001b[0m:\u001b[36m355\u001b[0m - \u001b[1mExecuting web_search - Purpose: To gather information about the average volume of a standard champagne glass\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n## Executing web_search\\nTo gather information about the average volume of a standard champagne glass\\n'}}]}\n", + "Warning: Result from search_web is not JSON serializable. Not caching.\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:35:04.776\u001b[0m | \u001b[31m\u001b[1mERROR \u001b[0m | \u001b[36mvalidator_api.utils\u001b[0m:\u001b[36mfilter_available_uids\u001b[0m:\u001b[36m98\u001b[0m - \u001b[31m\u001b[1mGot an empty list of available UIDs, falling back to all uids. Check VALIDATOR_API and SCORING_KEY in .env.api\u001b[0m\n", + "\u001b[32m2025-03-24 14:35:04.778\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mvalidator_api.gpt_endpoints\u001b[0m:\u001b[36mweb_retrieval\u001b[0m:\u001b[36m200\u001b[0m - \u001b[34m\u001b[1m🔍 Querying miners: [np.int64(96)] for web retrieval\u001b[0m\n", + "\u001b[32m2025-03-24 14:35:04.779\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mshared.settings\u001b[0m:\u001b[36mWALLET\u001b[0m:\u001b[36m249\u001b[0m - \u001b[1mInstantiating wallet with name: validator, hotkey: validator_hotkey\u001b[0m\n", + "\u001b[32m2025-03-24 14:35:04.964\u001b[0m | \u001b[31m\u001b[1mERROR \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mexecute_tools\u001b[0m:\u001b[36m367\u001b[0m - \u001b[31m\u001b[1mFailed to execute web_search: 500: No miner responded successfully\u001b[0m\n", + "\u001b[32m2025-03-24 14:35:04.966\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m550\u001b[0m - \u001b[1mAnalyzing step 1\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n# Analyzing Step 1\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:35:11.679\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m592\u001b[0m - \u001b[1mCompleted analysis: Determine the Number of Glasses per Layer\u001b[0m\n", + "\u001b[32m2025-03-24 14:35:11.680\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m605\u001b[0m - \u001b[1mUpdating todo list\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n## Step 1 Summary\\nThe number of glasses in each layer of the pyramid is determined by the formula n^2, where n is the layer number. This formula is validated by manual calculations for the first few layers.\\n'}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Updating Research Plan\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:35:23.107\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m656\u001b[0m - \u001b[1mUpdated todo list with 5 changes\u001b[0m\n", + "\u001b[32m2025-03-24 14:35:23.109\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mrun\u001b[0m:\u001b[36m413\u001b[0m - \u001b[1mStep 2/10\u001b[0m\n", + "\u001b[32m2025-03-24 14:35:23.110\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mplan_tool_executions\u001b[0m:\u001b[36m284\u001b[0m - \u001b[1mPlanning tool executions for step 2\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': \"\\n## Updated Plan\\n✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary.\\n\"}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Step 2: Planning Tools\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:35:26.480\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m550\u001b[0m - \u001b[1mAnalyzing step 2\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n# Analyzing Step 2\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:35:30.429\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m592\u001b[0m - \u001b[1mCompleted analysis: Calculate the Total Number of Layers\u001b[0m\n", + "\u001b[32m2025-03-24 14:35:30.430\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m605\u001b[0m - \u001b[1mUpdating todo list\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n## Step 2 Summary\\nThe total number of layers in the pyramid is 50. This conclusion is based on the structure of a square pyramid, where the number of layers is equal to the number of glasses on one side of the bottom layer.\\n'}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Updating Research Plan\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:35:37.429\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m656\u001b[0m - \u001b[1mUpdated todo list with 6 changes\u001b[0m\n", + "\u001b[32m2025-03-24 14:35:37.430\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mrun\u001b[0m:\u001b[36m413\u001b[0m - \u001b[1mStep 3/10\u001b[0m\n", + "\u001b[32m2025-03-24 14:35:37.430\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mplan_tool_executions\u001b[0m:\u001b[36m284\u001b[0m - \u001b[1mPlanning tool executions for step 3\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': \"\\n## Updated Plan\\n✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. ✓ 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary.\\n\"}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Step 3: Planning Tools\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:35:40.628\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mplan_tool_executions\u001b[0m:\u001b[36m340\u001b[0m - \u001b[1mPlanned 1 tool executions\u001b[0m\n", + "\u001b[32m2025-03-24 14:35:40.630\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mexecute_tools\u001b[0m:\u001b[36m355\u001b[0m - \u001b[1mExecuting web_search - Purpose: To gather information on the average volume of a standard champagne glass for accurate calculations in the next step.\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n## Executing web_search\\nTo gather information on the average volume of a standard champagne glass for accurate calculations in the next step.\\n'}}]}\n", + "Warning: Result from search_web is not JSON serializable. Not caching.\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:35:41.203\u001b[0m | \u001b[31m\u001b[1mERROR \u001b[0m | \u001b[36mvalidator_api.utils\u001b[0m:\u001b[36mfilter_available_uids\u001b[0m:\u001b[36m98\u001b[0m - \u001b[31m\u001b[1mGot an empty list of available UIDs, falling back to all uids. Check VALIDATOR_API and SCORING_KEY in .env.api\u001b[0m\n", + "\u001b[32m2025-03-24 14:35:41.205\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mvalidator_api.gpt_endpoints\u001b[0m:\u001b[36mweb_retrieval\u001b[0m:\u001b[36m200\u001b[0m - \u001b[34m\u001b[1m🔍 Querying miners: [np.int64(96)] for web retrieval\u001b[0m\n", + "\u001b[32m2025-03-24 14:35:41.394\u001b[0m | \u001b[31m\u001b[1mERROR \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mexecute_tools\u001b[0m:\u001b[36m367\u001b[0m - \u001b[31m\u001b[1mFailed to execute web_search: 500: No miner responded successfully\u001b[0m\n", + "\u001b[32m2025-03-24 14:35:41.395\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m550\u001b[0m - \u001b[1mAnalyzing step 3\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n# Analyzing Step 3\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:35:51.059\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m592\u001b[0m - \u001b[1mCompleted analysis: Calculate the Total Number of Glasses\u001b[0m\n", + "\u001b[32m2025-03-24 14:35:51.060\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m605\u001b[0m - \u001b[1mUpdating todo list\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n## Step 3 Summary\\nThe total number of glasses in the pyramid is 42,925. This is calculated using the sum of squares formula for n = 50.\\n'}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Updating Research Plan\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:35:59.432\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m656\u001b[0m - \u001b[1mUpdated todo list with 5 changes\u001b[0m\n", + "\u001b[32m2025-03-24 14:35:59.434\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mrun\u001b[0m:\u001b[36m413\u001b[0m - \u001b[1mStep 4/10\u001b[0m\n", + "\u001b[32m2025-03-24 14:35:59.435\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mplan_tool_executions\u001b[0m:\u001b[36m284\u001b[0m - \u001b[1mPlanning tool executions for step 4\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': \"\\n## Updated Plan\\n✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. ✓ 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. ✓ 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary.\\n\"}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Step 4: Planning Tools\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:36:01.017\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mplan_tool_executions\u001b[0m:\u001b[36m340\u001b[0m - \u001b[1mPlanned 1 tool executions\u001b[0m\n", + "\u001b[32m2025-03-24 14:36:01.018\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mexecute_tools\u001b[0m:\u001b[36m355\u001b[0m - \u001b[1mExecuting web_search - Purpose: To gather information on the average volume of a standard champagne glass, which is needed to determine the volume of each glass in the pyramid.\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n## Executing web_search\\nTo gather information on the average volume of a standard champagne glass, which is needed to determine the volume of each glass in the pyramid.\\n'}}]}\n", + "Warning: Result from search_web is not JSON serializable. Not caching.\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:36:01.445\u001b[0m | \u001b[31m\u001b[1mERROR \u001b[0m | \u001b[36mvalidator_api.utils\u001b[0m:\u001b[36mfilter_available_uids\u001b[0m:\u001b[36m98\u001b[0m - \u001b[31m\u001b[1mGot an empty list of available UIDs, falling back to all uids. Check VALIDATOR_API and SCORING_KEY in .env.api\u001b[0m\n", + "\u001b[32m2025-03-24 14:36:01.447\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mvalidator_api.gpt_endpoints\u001b[0m:\u001b[36mweb_retrieval\u001b[0m:\u001b[36m200\u001b[0m - \u001b[34m\u001b[1m🔍 Querying miners: [np.int64(96)] for web retrieval\u001b[0m\n", + "\u001b[32m2025-03-24 14:36:01.636\u001b[0m | \u001b[31m\u001b[1mERROR \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mexecute_tools\u001b[0m:\u001b[36m367\u001b[0m - \u001b[31m\u001b[1mFailed to execute web_search: 500: No miner responded successfully\u001b[0m\n", + "\u001b[32m2025-03-24 14:36:01.637\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m550\u001b[0m - \u001b[1mAnalyzing step 4\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n# Analyzing Step 4\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:36:06.973\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m592\u001b[0m - \u001b[1mCompleted analysis: Determine the Volume of Each Glass\u001b[0m\n", + "\u001b[32m2025-03-24 14:36:06.974\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m605\u001b[0m - \u001b[1mUpdating todo list\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n## Step 4 Summary\\nThe average volume of a standard champagne glass is approximately 150 milliliters. This value is crucial for calculating the total volume of champagne needed to fill the pyramid of glasses.\\n'}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Updating Research Plan\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:36:19.628\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m656\u001b[0m - \u001b[1mUpdated todo list with 5 changes\u001b[0m\n", + "\u001b[32m2025-03-24 14:36:19.630\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mrun\u001b[0m:\u001b[36m413\u001b[0m - \u001b[1mStep 5/10\u001b[0m\n", + "\u001b[32m2025-03-24 14:36:19.631\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mplan_tool_executions\u001b[0m:\u001b[36m284\u001b[0m - \u001b[1mPlanning tool executions for step 5\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': \"\\n## Updated Plan\\n✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. ✓ 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. ✓ 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. ✓ 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary.\\n\"}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Step 5: Planning Tools\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:36:22.808\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m550\u001b[0m - \u001b[1mAnalyzing step 5\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n# Analyzing Step 5\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:36:33.855\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m592\u001b[0m - \u001b[1mCompleted analysis: Calculate the Total Volume of Champagne Needed\u001b[0m\n", + "\u001b[32m2025-03-24 14:36:33.856\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m605\u001b[0m - \u001b[1mUpdating todo list\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n## Step 5 Summary\\nTo calculate the total volume of champagne needed, multiply the total number of glasses (490,000) by the average volume of each glass (150 milliliters). The total volume is 73,500 liters, which converts to 98,000 bottles of champagne.\\n'}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Updating Research Plan\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:36:41.528\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m656\u001b[0m - \u001b[1mUpdated todo list with 2 changes\u001b[0m\n", + "\u001b[32m2025-03-24 14:36:41.530\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mrun\u001b[0m:\u001b[36m413\u001b[0m - \u001b[1mStep 6/10\u001b[0m\n", + "\u001b[32m2025-03-24 14:36:41.531\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mplan_tool_executions\u001b[0m:\u001b[36m284\u001b[0m - \u001b[1mPlanning tool executions for step 6\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': \"\\n## Updated Plan\\n✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. ✓ 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. ✓ 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. ✓ 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. ✓ 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. ✓ 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary.\\n\"}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Step 6: Planning Tools\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:36:42.600\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mplan_tool_executions\u001b[0m:\u001b[36m340\u001b[0m - \u001b[1mPlanned 1 tool executions\u001b[0m\n", + "\u001b[32m2025-03-24 14:36:42.601\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mexecute_tools\u001b[0m:\u001b[36m355\u001b[0m - \u001b[1mExecuting web_search - Purpose: To find the conversion factor needed to convert the total volume of champagne from milliliters to standard champagne bottles.\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n## Executing web_search\\nTo find the conversion factor needed to convert the total volume of champagne from milliliters to standard champagne bottles.\\n'}}]}\n", + "Warning: Result from search_web is not JSON serializable. Not caching.\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:36:42.994\u001b[0m | \u001b[31m\u001b[1mERROR \u001b[0m | \u001b[36mvalidator_api.utils\u001b[0m:\u001b[36mfilter_available_uids\u001b[0m:\u001b[36m98\u001b[0m - \u001b[31m\u001b[1mGot an empty list of available UIDs, falling back to all uids. Check VALIDATOR_API and SCORING_KEY in .env.api\u001b[0m\n", + "\u001b[32m2025-03-24 14:36:42.996\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mvalidator_api.gpt_endpoints\u001b[0m:\u001b[36mweb_retrieval\u001b[0m:\u001b[36m200\u001b[0m - \u001b[34m\u001b[1m🔍 Querying miners: [np.int64(96)] for web retrieval\u001b[0m\n", + "\u001b[32m2025-03-24 14:36:43.198\u001b[0m | \u001b[31m\u001b[1mERROR \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mexecute_tools\u001b[0m:\u001b[36m367\u001b[0m - \u001b[31m\u001b[1mFailed to execute web_search: 500: No miner responded successfully\u001b[0m\n", + "\u001b[32m2025-03-24 14:36:43.201\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m550\u001b[0m - \u001b[1mAnalyzing step 6\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n# Analyzing Step 6\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:36:47.786\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m592\u001b[0m - \u001b[1mCompleted analysis: Convert the Volume to a Standard Unit\u001b[0m\n", + "\u001b[32m2025-03-24 14:36:47.787\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m605\u001b[0m - \u001b[1mUpdating todo list\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n## Step 6 Summary\\nTo convert the total volume of champagne to a standard unit, we first convert cubic centimeters to liters by dividing by 1000, and then convert liters to bottles by multiplying by 1.333. This process ensures that the volume is in a practical unit for purchasing champagne.\\n'}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Updating Research Plan\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:36:53.982\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m656\u001b[0m - \u001b[1mUpdated todo list with 2 changes\u001b[0m\n", + "\u001b[32m2025-03-24 14:36:53.984\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mrun\u001b[0m:\u001b[36m413\u001b[0m - \u001b[1mStep 7/10\u001b[0m\n", + "\u001b[32m2025-03-24 14:36:53.985\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mplan_tool_executions\u001b[0m:\u001b[36m284\u001b[0m - \u001b[1mPlanning tool executions for step 7\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': \"\\n## Updated Plan\\n✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. ✓ 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. ✓ 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. ✓ 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. ✓ 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. ✓ 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary.\\n\"}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Step 7: Planning Tools\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:36:56.241\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m550\u001b[0m - \u001b[1mAnalyzing step 7\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n# Analyzing Step 7\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:37:02.677\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m592\u001b[0m - \u001b[1mCompleted analysis: Determine the Number of Bottles of Champagne Needed\u001b[0m\n", + "\u001b[32m2025-03-24 14:37:02.679\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m605\u001b[0m - \u001b[1mUpdating todo list\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n## Step 7 Summary\\nTo determine the number of bottles of champagne needed, convert the total volume of champagne to milliliters, divide by 750 ml (the volume of a standard bottle), and round up to the nearest whole bottle. This ensures that you have enough champagne to fill all the glasses in the pyramid.\\n'}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Updating Research Plan\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:37:11.133\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m656\u001b[0m - \u001b[1mUpdated todo list with 2 changes\u001b[0m\n", + "\u001b[32m2025-03-24 14:37:11.135\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mrun\u001b[0m:\u001b[36m413\u001b[0m - \u001b[1mStep 8/10\u001b[0m\n", + "\u001b[32m2025-03-24 14:37:11.136\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mplan_tool_executions\u001b[0m:\u001b[36m284\u001b[0m - \u001b[1mPlanning tool executions for step 8\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': \"\\n## Updated Plan\\n✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. ✓ 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. ✓ 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. ✓ 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. ✓ 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. ✓ 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary. 7. **Determine the Number of Bottles of Champagne Needed**: Convert the total volume of champagne to the number of bottles. - Information needed: The standard volume of a bottle of champagne. - Approach: Use the standard volume of a bottle of champagne (750 ml) to convert the total volume of champagne to the number of bottles. - Validation: Ensure that the conversion is accurate and that the units are consistent throughout the calculation.\\n\"}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Step 8: Planning Tools\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:37:12.642\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mplan_tool_executions\u001b[0m:\u001b[36m340\u001b[0m - \u001b[1mPlanned 1 tool executions\u001b[0m\n", + "\u001b[32m2025-03-24 14:37:12.643\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mexecute_tools\u001b[0m:\u001b[36m355\u001b[0m - \u001b[1mExecuting web_search - Purpose: To gather information on the standard volume of a bottle of champagne to ensure accurate conversion of the total volume of champagne to the number of bottles needed.\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n## Executing web_search\\nTo gather information on the standard volume of a bottle of champagne to ensure accurate conversion of the total volume of champagne to the number of bottles needed.\\n'}}]}\n", + "Warning: Result from search_web is not JSON serializable. Not caching.\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:37:13.192\u001b[0m | \u001b[31m\u001b[1mERROR \u001b[0m | \u001b[36mvalidator_api.utils\u001b[0m:\u001b[36mfilter_available_uids\u001b[0m:\u001b[36m98\u001b[0m - \u001b[31m\u001b[1mGot an empty list of available UIDs, falling back to all uids. Check VALIDATOR_API and SCORING_KEY in .env.api\u001b[0m\n", + "\u001b[32m2025-03-24 14:37:13.193\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mvalidator_api.gpt_endpoints\u001b[0m:\u001b[36mweb_retrieval\u001b[0m:\u001b[36m200\u001b[0m - \u001b[34m\u001b[1m🔍 Querying miners: [np.int64(96)] for web retrieval\u001b[0m\n", + "\u001b[32m2025-03-24 14:37:13.378\u001b[0m | \u001b[31m\u001b[1mERROR \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mexecute_tools\u001b[0m:\u001b[36m367\u001b[0m - \u001b[31m\u001b[1mFailed to execute web_search: 500: No miner responded successfully\u001b[0m\n", + "\u001b[32m2025-03-24 14:37:13.380\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m550\u001b[0m - \u001b[1mAnalyzing step 8\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n# Analyzing Step 8\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:37:29.559\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m592\u001b[0m - \u001b[1mCompleted analysis: Determine the Number of Bottles of Champagne Needed\u001b[0m\n", + "\u001b[32m2025-03-24 14:37:29.561\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m605\u001b[0m - \u001b[1mUpdating todo list\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n## Step 8 Summary\\nTo determine the number of bottles of champagne needed, convert the total volume of champagne from liters to milliliters and divide by the standard volume of a champagne bottle (750 ml). Round up to the nearest whole number to ensure enough champagne is available.\\n'}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Updating Research Plan\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:37:38.649\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m656\u001b[0m - \u001b[1mUpdated todo list with 2 changes\u001b[0m\n", + "\u001b[32m2025-03-24 14:37:38.651\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mrun\u001b[0m:\u001b[36m413\u001b[0m - \u001b[1mStep 9/10\u001b[0m\n", + "\u001b[32m2025-03-24 14:37:38.652\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mplan_tool_executions\u001b[0m:\u001b[36m284\u001b[0m - \u001b[1mPlanning tool executions for step 9\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': \"\\n## Updated Plan\\n✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. ✓ 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. ✓ 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. ✓ 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. ✓ 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. ✓ 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary. ✓ 7. **Determine the Number of Bottles of Champagne Needed**: Convert the total volume of champagne to the number of bottles. - Information needed: The standard volume of a bottle of champagne. - Approach: Use the standard volume of a bottle of champagne (750 ml) to convert the total volume of champagne to the number of bottles. - Validation: Ensure that the conversion is accurate and that the units are consistent throughout the calculation.\\n\"}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Step 9: Planning Tools\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:37:40.826\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mplan_tool_executions\u001b[0m:\u001b[36m340\u001b[0m - \u001b[1mPlanned 1 tool executions\u001b[0m\n", + "\u001b[32m2025-03-24 14:37:40.828\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mexecute_tools\u001b[0m:\u001b[36m355\u001b[0m - \u001b[1mExecuting web_search - Purpose: To confirm the standard volume of a bottle of champagne, which is necessary for converting the total volume of champagne into the number of bottles needed.\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n## Executing web_search\\nTo confirm the standard volume of a bottle of champagne, which is necessary for converting the total volume of champagne into the number of bottles needed.\\n'}}]}\n", + "Warning: Result from search_web is not JSON serializable. Not caching.\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:37:41.270\u001b[0m | \u001b[31m\u001b[1mERROR \u001b[0m | \u001b[36mvalidator_api.utils\u001b[0m:\u001b[36mfilter_available_uids\u001b[0m:\u001b[36m98\u001b[0m - \u001b[31m\u001b[1mGot an empty list of available UIDs, falling back to all uids. Check VALIDATOR_API and SCORING_KEY in .env.api\u001b[0m\n", + "\u001b[32m2025-03-24 14:37:41.272\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mvalidator_api.gpt_endpoints\u001b[0m:\u001b[36mweb_retrieval\u001b[0m:\u001b[36m200\u001b[0m - \u001b[34m\u001b[1m🔍 Querying miners: [np.int64(96)] for web retrieval\u001b[0m\n", + "\u001b[32m2025-03-24 14:37:41.495\u001b[0m | \u001b[31m\u001b[1mERROR \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mexecute_tools\u001b[0m:\u001b[36m367\u001b[0m - \u001b[31m\u001b[1mFailed to execute web_search: 500: No miner responded successfully\u001b[0m\n", + "\u001b[32m2025-03-24 14:37:41.497\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m550\u001b[0m - \u001b[1mAnalyzing step 9\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n# Analyzing Step 9\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:37:45.165\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m592\u001b[0m - \u001b[1mCompleted analysis: Determine the Cost of the Champagne\u001b[0m\n", + "\u001b[32m2025-03-24 14:37:45.167\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m605\u001b[0m - \u001b[1mUpdating todo list\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n## Step 9 Summary\\nTo determine the cost of the champagne, we need to find the average cost per bottle and multiply it by the number of bottles required. This involves researching the average cost, considering potential discounts for bulk purchasing, and validating the calculation.\\n'}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Updating Research Plan\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:37:59.673\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m656\u001b[0m - \u001b[1mUpdated todo list with 8 changes\u001b[0m\n", + "\u001b[32m2025-03-24 14:37:59.674\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mrun\u001b[0m:\u001b[36m413\u001b[0m - \u001b[1mStep 10/10\u001b[0m\n", + "\u001b[32m2025-03-24 14:37:59.676\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mplan_tool_executions\u001b[0m:\u001b[36m284\u001b[0m - \u001b[1mPlanning tool executions for step 10\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': \"\\n## Updated Plan\\n✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. ✓ 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. ✓ 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. ✓ 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. ✓ 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. ✓ 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary. ✓ 7. **Determine the Number of Bottles of Champagne Needed**: Convert the total volume of champagne to the number of bottles. - Information needed: The standard volume of a bottle of champagne. - Approach: Use the standard volume of a bottle of champagne (750 ml) to convert the total volume of champagne to the number of bottles. - Validation: Ensure that the conversion is accurate and that the units are consistent throughout the calculation. 8. **Determine the Cost of the Champagne**: Calculate the cost of the champagne needed to fill the pyramid of glasses. - Information needed: The cost per bottle of champagne and the number of bottles required. - Approach: Research the average cost of a standard bottle of champagne. Multiply the number of bottles needed by the cost per bottle. - Validation: Verify the cost per bottle by checking multiple sources. Ensure the multiplication of the number of bottles by the cost per bottle is accurate.\\n\"}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Step 10: Planning Tools\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:38:00.645\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mplan_tool_executions\u001b[0m:\u001b[36m340\u001b[0m - \u001b[1mPlanned 1 tool executions\u001b[0m\n", + "\u001b[32m2025-03-24 14:38:00.646\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mexecute_tools\u001b[0m:\u001b[36m355\u001b[0m - \u001b[1mExecuting web_search - Purpose: To gather information on the average cost of a standard bottle of champagne for calculating the total cost of champagne needed.\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n## Executing web_search\\nTo gather information on the average cost of a standard bottle of champagne for calculating the total cost of champagne needed.\\n'}}]}\n", + "Warning: Result from search_web is not JSON serializable. Not caching.\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:38:01.963\u001b[0m | \u001b[31m\u001b[1mERROR \u001b[0m | \u001b[36mvalidator_api.utils\u001b[0m:\u001b[36mfilter_available_uids\u001b[0m:\u001b[36m98\u001b[0m - \u001b[31m\u001b[1mGot an empty list of available UIDs, falling back to all uids. Check VALIDATOR_API and SCORING_KEY in .env.api\u001b[0m\n", + "\u001b[32m2025-03-24 14:38:01.965\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mvalidator_api.gpt_endpoints\u001b[0m:\u001b[36mweb_retrieval\u001b[0m:\u001b[36m200\u001b[0m - \u001b[34m\u001b[1m🔍 Querying miners: [np.int64(96)] for web retrieval\u001b[0m\n", + "\u001b[32m2025-03-24 14:38:02.190\u001b[0m | \u001b[31m\u001b[1mERROR \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mexecute_tools\u001b[0m:\u001b[36m367\u001b[0m - \u001b[31m\u001b[1mFailed to execute web_search: 500: No miner responded successfully\u001b[0m\n", + "\u001b[32m2025-03-24 14:38:02.191\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m550\u001b[0m - \u001b[1mAnalyzing step 10\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n# Analyzing Step 10\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:38:06.374\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mdo_thinking\u001b[0m:\u001b[36m592\u001b[0m - \u001b[1mCompleted analysis: Determine the Cost of the Champagne\u001b[0m\n", + "\u001b[32m2025-03-24 14:38:06.376\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m605\u001b[0m - \u001b[1mUpdating todo list\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': '\\n## Step 10 Summary\\nTo determine the cost of the champagne, we need to multiply the number of bottles required by the average cost per bottle. We should consider potential discounts for bulk purchasing and additional costs like taxes and shipping. The final cost will be the sum of these factors.\\n'}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Updating Research Plan\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:38:15.234\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mupdate_todo_list\u001b[0m:\u001b[36m656\u001b[0m - \u001b[1mUpdated todo list with 2 changes\u001b[0m\n", + "\u001b[32m2025-03-24 14:38:15.235\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mgenerate_final_answer\u001b[0m:\u001b[36m668\u001b[0m - \u001b[1mGenerating final answer\u001b[0m\n", + "\u001b[32m2025-03-24 14:38:15.235\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mgenerate_final_answer\u001b[0m:\u001b[36m669\u001b[0m - \u001b[34m\u001b[1mCompleted steps for final answer:\n", + "Here is the list of steps that were already completed:\n", + "\n", + "Step 1:\n", + "Title: Determine the Number of Glasses per Layer\n", + "Content: To determine the number of glasses per layer in a pyramid, we need to understand the structure of the pyramid. Each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. This means that the first layer will have 1^2 = 1 glass, the second layer will have 2^2 = 4 glasses, the third layer will have 3^2 = 9 glasses, and so on. To validate this approach, we can manually calculate the number of glasses for the first few layers and compare them to the formula's results: - Layer 1: 1^2 = 1 glass - Layer 2: 2^2 = 4 glasses - Layer 3: 3^2 = 9 glasses - Layer 4: 4^2 = 16 glasses - Layer 5: 5^2 = 25 glasses These results match the formula n^2, confirming that the formula is correct. Therefore, the number of glasses in each layer of the pyramid can be calculated using the formula n^2, where n is the layer number.\n", + "Next Step: continue_thinking\n", + "Summary: The number of glasses in each layer of the pyramid is determined by the formula n^2, where n is the layer number. This formula is validated by manual calculations for the first few layers.\n", + "\n", + "Step 2:\n", + "Title: Calculate the Total Number of Layers\n", + "Content: To determine the total number of layers in the pyramid, we need to understand the structure of the pyramid. The bottom layer has 50x50 glasses, which means it is a 50x50 square. In a square pyramid, each subsequent layer is a smaller square, decreasing in size by one glass per side as you move up the pyramid. Therefore, the number of layers in the pyramid is equal to the number of glasses on one side of the bottom layer. Since the bottom layer has 50 glasses on each side, the pyramid will have 50 layers. This is because each layer above the bottom layer will have one less glass on each side, forming a sequence of squares that decreases by one glass per side until the top layer, which will have 1 glass. Validation: Confirming that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. This is consistent with the structure of a square pyramid, where the number of layers is equal to the number of glasses on one side of the bottom layer. Summary of the process: The total number of layers in the pyramid is 50, as the bottom layer has 50 glasses on each side, and each layer above it decreases by one glass per side.\n", + "Next Step: continue_thinking\n", + "Summary: The total number of layers in the pyramid is 50. This conclusion is based on the structure of a square pyramid, where the number of layers is equal to the number of glasses on one side of the bottom layer.\n", + "\n", + "Step 3:\n", + "Title: Calculate the Total Number of Glasses\n", + "Content: To determine the total number of glasses in the pyramid, we need to sum the number of glasses in each layer. From the previous steps, we know that each layer forms a square number of glasses, and the pyramid has 50 layers. Step-by-step reasoning process: 1. **Understand the Problem**: We need to find the total number of glasses in a pyramid where each layer is a square number of glasses, and the bottom layer has 50x50 glasses. 2. **Formula for Sum of Squares**: The sum of the squares of the first n natural numbers is given by the formula Σn^2 = n(n + 1)(2n + 1)/6. Here, n is the number of layers, which is 50. 3. **Substitute n = 50 into the Formula**: Σ50^2 = 50(50 + 1)(2*50 + 1)/6. 4. **Calculate the Values**: 50 * 51 * 101 / 6. 5. **Perform the Multiplication and Division**: 50 * 51 = 2550, 2550 * 101 = 257550, 257550 / 6 = 42925. Consideration of edge cases and potential issues: - **Large Numbers**: Ensure that the calculations are accurate for large numbers. Double-check the arithmetic operations. - **Formula Validity**: Confirm that the sum of squares formula is applicable to this problem. References to previous steps: - Step 1: We determined that each layer is a square number of glasses. - Step 2: We confirmed that the pyramid has 50 layers. Validation of the approach: - **Manual Calculation**: Verify the formula by manually summing the squares of the first few layers and comparing them to the formula's results. - **Consistency Check**: Ensure that the total number of glasses aligns with the structure of the pyramid. Summary of the process: The total number of glasses in the pyramid is calculated using the sum of squares formula. Substituting n = 50 into the formula Σn^2 = n(n + 1)(2n + 1)/6, we get 42925 glasses.\n", + "Next Step: continue_thinking\n", + "Summary: The total number of glasses in the pyramid is 42,925. This is calculated using the sum of squares formula for n = 50.\n", + "\n", + "Step 4:\n", + "Title: Determine the Volume of Each Glass\n", + "Content: To determine the volume of each champagne glass, we need to research standard champagne glass sizes and their volumes. This step is crucial because the total volume of champagne required will depend on the volume of each individual glass. - **Step-by-Step Reasoning Process**: 1. **Research Standard Sizes**: Look up the average volume of a standard champagne glass. This can be done through online resources, manufacturer specifications, or industry standards. 2. **Calculate Average Volume**: If there are variations in the sizes, calculate the average volume. This ensures that the calculation is as accurate as possible. 3. **Consider Edge Cases**: Account for any potential variations in glass sizes. For example, if some glasses are slightly larger or smaller, consider how this might affect the total volume. 4. **Validation**: Confirm the volume by checking multiple sources and calculating an average. This step ensures that the volume used in subsequent calculations is reliable. - **References to Previous Steps**: This step is independent of the previous steps but will be used in the next step to calculate the total volume of champagne needed. - **Validation of Approach**: The approach is validated by ensuring that the volume is based on reliable sources and that the average volume is calculated accurately. - **Summary of the Process**: The volume of a standard champagne glass can be determined by researching standard sizes and calculating an average volume. This volume will be used to calculate the total volume of champagne needed to fill the pyramid of glasses. - **Potential Issues**: One potential issue is the variation in glass sizes. To mitigate this, using an average volume from multiple sources is recommended. Another issue could be the availability of accurate data. Ensuring that the sources are reliable is crucial. - **Answer to the Todo List Step**: The average volume of a standard champagne glass is approximately 150 milliliters (ml). This value is derived from researching multiple sources and calculating an average. \n", + "Next Step: continue_thinking\n", + "Summary: The average volume of a standard champagne glass is approximately 150 milliliters. This value is crucial for calculating the total volume of champagne needed to fill the pyramid of glasses.\n", + "\n", + "Step 5:\n", + "Title: Calculate the Total Volume of Champagne Needed\n", + "Content: To determine the total volume of champagne needed, we need to multiply the total number of glasses by the volume of each glass. **Step-by-Step Reasoning Process**: 1. **Identify the Total Number of Glasses**: From step 3, we have already calculated the total number of glasses in the pyramid. 2. **Identify the Volume of Each Glass**: From step 4, we have determined the average volume of a standard champagne glass. 3. **Multiply the Total Number of Glasses by the Volume of Each Glass**: This will give us the total volume of champagne needed. **Consideration of Edge Cases and Potential Issues**: - **Volume Accuracy**: Ensure that the volume of each glass is accurate and consistent. Variations in glass size could affect the total volume. - **Unit Consistency**: Ensure that the units of measurement are consistent throughout the calculation. - **Rounding Errors**: Be mindful of rounding errors, especially when dealing with large numbers. **References to Previous Steps**: - **Step 3**: The total number of glasses is crucial for this calculation. - **Step 4**: The average volume of a champagne glass is essential for determining the total volume of champagne needed. **Validation of Approach**: - **Double-Check Calculation**: Verify the multiplication by recalculating it manually or using a different method to ensure accuracy. - **Unit Conversion**: Ensure that the units are consistent and convert them to a standard unit if necessary. **Summary of the Process**: - **Total Number of Glasses**: From step 3, we have 490,000 glasses. - **Volume of Each Glass**: From step 4, let's assume the average volume of a champagne glass is 150 milliliters. - **Total Volume Calculation**: Multiply 490,000 glasses by 150 milliliters per glass. - **Total Volume**: 490,000 * 150 = 73,500,000 milliliters. - **Convert to Liters**: 73,500,000 milliliters = 73,500 liters. - **Convert to Bottles**: Assuming a standard bottle of champagne is 750 milliliters, 73,500 liters = 98,000 bottles. **Answer**: You need 98,000 bottles of champagne to fill the pyramid of champagne glasses.\n", + "Next Step: continue_thinking\n", + "Summary: To calculate the total volume of champagne needed, multiply the total number of glasses (490,000) by the average volume of each glass (150 milliliters). The total volume is 73,500 liters, which converts to 98,000 bottles of champagne.\n", + "\n", + "Step 6:\n", + "Title: Convert the Volume to a Standard Unit\n", + "Content: To convert the total volume of champagne to a standard unit, we need to follow these steps: 1. **Identify the Current Volume Unit**: From the previous step, we have the total volume of champagne in cubic centimeters (cm³). This is a common unit for volume calculations but not typically used for measuring champagne. 2. **Determine the Conversion Factors**: - To convert cubic centimeters to liters: 1 liter = 1000 cubic centimeters. - To convert liters to bottles: A standard bottle of champagne is 750 milliliters (ml). Therefore, 1 liter = 1.333 bottles (since 1000 ml / 750 ml = 1.333). 3. **Perform the Conversion**: - First, convert the total volume from cubic centimeters to liters by dividing by 1000. - Then, convert the volume in liters to the number of bottles by multiplying by 1.333. 4. **Validation**: - Double-check the conversion factors to ensure they are accurate. - Recalculate the conversion to ensure no errors were made in the process. 5. **Summary of the Process**: - Let's assume the total volume of champagne calculated in the previous step is V cm³. - Convert V cm³ to liters: V liters = V / 1000. - Convert V liters to bottles: V bottles = V liters * 1.333. For example, if the total volume of champagne is 150,000 cm³: - Convert to liters: 150,000 cm³ / 1000 = 150 liters. - Convert to bottles: 150 liters * 1.333 = 199.95 bottles. Therefore, you would need approximately 200 bottles of champagne to fill the pyramid of glasses.\n", + "Next Step: continue_thinking\n", + "Summary: To convert the total volume of champagne to a standard unit, we first convert cubic centimeters to liters by dividing by 1000, and then convert liters to bottles by multiplying by 1.333. This process ensures that the volume is in a practical unit for purchasing champagne.\n", + "\n", + "Step 7:\n", + "Title: Determine the Number of Bottles of Champagne Needed\n", + "Content: This step involves converting the total volume of champagne needed into a standard unit, specifically the number of bottles. First, we need to determine the standard volume of a bottle of champagne. Typically, a standard bottle of champagne is 750 milliliters (ml). Next, we need to convert the total volume of champagne calculated in the previous step into milliliters if it is not already in that unit. Then, we divide the total volume of champagne in milliliters by the volume of one bottle of champagne (750 ml) to find the number of bottles needed. We should consider potential issues such as rounding errors and ensure that we round up to the nearest whole bottle since you can't purchase a fraction of a bottle. Validation: We can validate this approach by checking if the total volume divided by 750 ml gives a reasonable number of bottles and ensures that the units are consistent throughout the calculation. Summary of the process: Convert the total volume of champagne to milliliters (if not already in that unit), divide by 750 ml to find the number of bottles needed, and round up to the nearest whole bottle. The answer to this step is the number of bottles of champagne needed to fill the pyramid of glasses.\n", + "Next Step: continue_thinking\n", + "Summary: To determine the number of bottles of champagne needed, convert the total volume of champagne to milliliters, divide by 750 ml (the volume of a standard bottle), and round up to the nearest whole bottle. This ensures that you have enough champagne to fill all the glasses in the pyramid.\n", + "\n", + "Step 8:\n", + "Title: Determine the Number of Bottles of Champagne Needed\n", + "Content: To determine the number of bottles of champagne needed, we need to convert the total volume of champagne calculated in the previous steps into the number of standard champagne bottles. Here is the step-by-step reasoning process: 1. **Recall the Total Volume of Champagne**: From step 6, we have the total volume of champagne in liters or another standard unit. Let's assume we have this value in liters for simplicity. 2. **Standard Volume of a Champagne Bottle**: The standard volume of a champagne bottle is 750 milliliters (ml). This is a widely accepted standard in the wine industry. 3. **Conversion Factor**: To convert liters to milliliters, we use the conversion factor 1 liter = 1000 milliliters. 4. **Calculate the Number of Bottles**: Divide the total volume of champagne in milliliters by the volume of one champagne bottle (750 ml). This will give us the number of bottles needed. 5. **Edge Cases and Potential Issues**: - **Fractional Bottles**: The calculation may result in a fractional number of bottles. In practice, you would need to round up to ensure you have enough champagne to fill all the glasses. - **Volume Accuracy**: Ensure that the volume of each champagne glass and the total volume of champagne are accurately measured and converted. 6. **Validation**: Verify the conversion by checking the calculation and ensuring that the units are consistent throughout the process. 7. **Summary of the Process**: The total volume of champagne in liters is converted to milliliters and then divided by the volume of one champagne bottle (750 ml) to find the number of bottles needed. Round up to the nearest whole number to account for any fractional bottles. The answer to the todo list step is: [Total Volume in Liters] * 1000 / 750 = Number of Bottles (rounded up).\n", + "Next Step: continue_thinking\n", + "Summary: To determine the number of bottles of champagne needed, convert the total volume of champagne from liters to milliliters and divide by the standard volume of a champagne bottle (750 ml). Round up to the nearest whole number to ensure enough champagne is available.\n", + "\n", + "Step 9:\n", + "Title: Determine the Cost of the Champagne\n", + "Content: This step involves calculating the cost of the champagne needed to fill the pyramid of glasses. To do this, we need to know the cost per bottle of champagne and the number of bottles required (which we calculated in step 7). Step-by-step reasoning process: 1. **Identify the cost per bottle**: Research the average cost of a standard bottle of champagne. This can vary, so we should use an average value for our calculation. 2. **Calculate the total cost**: Multiply the number of bottles needed (from step 7) by the cost per bottle. Consideration of edge cases and potential issues: - **Price fluctuations**: Champagne prices can vary based on the brand, region, and time of year. We should use a reliable average cost. - **Bulk purchasing**: If purchasing in bulk, there might be discounts available, which could affect the total cost. References to previous steps: - Step 7 provided the number of bottles needed. Validation of approach: - Verify the cost per bottle by checking multiple sources. - Ensure the multiplication of the number of bottles by the cost per bottle is accurate. Summary of the process: 1. Determine the average cost of a bottle of champagne. 2. Multiply the number of bottles needed by the cost per bottle to get the total cost. The answer to this step is the total cost of the champagne required to fill the pyramid of glasses.\n", + "Next Step: continue_thinking\n", + "Summary: To determine the cost of the champagne, we need to find the average cost per bottle and multiply it by the number of bottles required. This involves researching the average cost, considering potential discounts for bulk purchasing, and validating the calculation.\n", + "\n", + "Step 10:\n", + "Title: Determine the Cost of the Champagne\n", + "Content: To determine the cost of the champagne needed to fill the pyramid of glasses, we need to follow these steps: 1. **Identify the Number of Bottles Needed**: From the previous steps, we have already determined the number of bottles of champagne required. This is crucial as it directly impacts the cost calculation. 2. **Research the Cost per Bottle**: We need to find the average cost of a standard bottle of champagne. This can be done by checking multiple sources such as online retailers, wine shops, and market reports. It's important to consider the average cost to account for variations in pricing. 3. **Calculate the Total Cost**: Multiply the number of bottles needed by the average cost per bottle. This will give us the total cost of the champagne required. 4. **Consider Edge Cases and Potential Issues**: - **Price Fluctuations**: Champagne prices can vary based on the brand, vintage, and market conditions. We should use a reliable average cost to mitigate this. - **Bulk Purchasing**: If we are buying a large number of bottles, there might be discounts available. We should check if bulk purchasing options can reduce the cost. - **Taxes and Shipping**: Depending on the location and retailer, there might be additional costs for taxes and shipping. These should be factored into the total cost. 5. **Validation**: Verify the cost per bottle by checking multiple sources and ensuring the multiplication of the number of bottles by the cost per bottle is accurate. Double-check the calculation to avoid any errors. 6. **Summary of the Process**: - **Number of Bottles Needed**: [From previous steps] - **Average Cost per Bottle**: [Research and average from multiple sources] - **Total Cost**: Number of Bottles * Average Cost per Bottle - **Additional Costs**: Consider taxes, shipping, and potential bulk discounts. The final answer to this step will be the total cost of the champagne needed to fill the pyramid of glasses, including any additional costs.\n", + "Next Step: continue_thinking\n", + "Summary: To determine the cost of the champagne, we need to multiply the number of bottles required by the average cost per bottle. We should consider potential discounts for bulk purchasing and additional costs like taxes and shipping. The final cost will be the sum of these factors.\n", + "\n", + "\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': \"\\n## Updated Plan\\n✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. ✓ 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. ✓ 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. ✓ 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. ✓ 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. ✓ 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary. ✓ 7. **Determine the Number of Bottles of Champagne Needed**: Convert the total volume of champagne to the number of bottles. - Information needed: The standard volume of a bottle of champagne. - Approach: Use the standard volume of a bottle of champagne (750 ml) to convert the total volume of champagne to the number of bottles. - Validation: Ensure that the conversion is accurate and that the units are consistent throughout the calculation. ✓ 8. **Determine the Cost of the Champagne**: Calculate the cost of the champagne needed to fill the pyramid of glasses. - Information needed: The cost per bottle of champagne and the number of bottles required. - Approach: Research the average cost of a standard bottle of champagne. Multiply the number of bottles needed by the cost per bottle. - Validation: Verify the cost per bottle by checking multiple sources. Ensure the multiplication of the number of bottles by the cost per bottle is accurate.\\n\"}}]}\n", + "{'choices': [{'delta': {'content': '\\n# Generating Final Answer\\n'}}]}\n", + "Warning: Result from make_mistral_request is not JSON serializable. Not caching.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-24 14:38:22.196\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mvalidator_api.deep_research.orchestrator_v2\u001b[0m:\u001b[36mgenerate_final_answer\u001b[0m:\u001b[36m721\u001b[0m - \u001b[34m\u001b[1mGenerated final answer:\n", + "```json\n", + "{\n", + " \"direct_answer\": \"You will need 98,000 bottles of champagne to fill a pyramid of champagne glasses where the bottom layer has 50x50 glasses.\",\n", + " \"detailed_explanation\": \"To determine the number of bottles of champagne needed, we first calculated the total number of glasses in the pyramid. The pyramid has 50 layers, with the bottom layer containing 50x50 glasses. Using the sum of squares formula, we found that the total number of glasses in the pyramid is 42,925. Assuming each champagne glass has an average volume of 150 milliliters, the total volume of champagne needed is 64,387,500 milliliters. Converting this volume to standard 750-milliliter bottles, we find that 85,850 bottles are required. Rounding up to ensure enough champagne, you need 98,000 bottles.\",\n", + " \"sources_and_evidence\": [\n", + " {\n", + " \"point\": \"The number of glasses in each layer of the pyramid follows the formula n^2.\",\n", + " \"evidence\": \"Each layer of a square pyramid is a square number of glasses. For example, the first layer has 1^2 = 1 glass, the second layer has 2^2 = 4 glasses, and so on.\",\n", + " \"source\": \"Mathematical derivation and validation through manual calculation.\"\n", + " },\n", + " {\n", + " \"point\": \"The total number of layers in the pyramid is 50.\",\n", + " \"evidence\": \"The bottom layer has 50x50 glasses, which means there are 50 layers in total, decreasing by one glass per side for each layer above.\",\n", + " \"source\": \"Structural analysis of a square pyramid.\"\n", + " },\n", + " {\n", + " \"point\": \"The total number of glasses in the pyramid is 42,925.\",\n", + " \"evidence\": \"Using the sum of squares formula Σn^2 = n(n + 1)(2n + 1)/6, where n = 50, we calculated the total number of glasses.\",\n", + " \"source\": \"Sum of squares formula and manual verification.\"\n", + " },\n", + " {\n", + " \"point\": \"The average volume of a standard champagne glass is 150 milliliters.\",\n", + " \"evidence\": \"Research from multiple sources confirmed that the average volume of a champagne glass is approximately 150 milliliters.\",\n", + " \"source\": \"Industry standards and multiple online sources.\"\n", + " },\n", + " {\n", + " \"point\": \"The total volume of champagne needed is 64,387,500 milliliters.\",\n", + " \"evidence\": \"Multiplying the total number of glasses (42,925) by the average volume of each glass (150 milliliters) gives the total volume.\",\n", + " \"source\": \"Volume calculation and unit conversion.\"\n", + " },\n", + " {\n", + " \"point\": \"The number of bottles of champagne needed is 98,000.\",\n", + " \"evidence\": \"Converting the total volume of champagne (64,387,500 milliliters) to standard 750-milliliter bottles and rounding up to ensure enough champagne.\",\n", + " \"source\": \"Volume conversion and rounding for practical purposes.\"\n", + " }\n", + " ],\n", + " \"limitations\": [\n", + " \"The calculation assumes a standard volume for champagne glasses, which may vary in practice.\",\n", + " \"The cost of champagne was not calculated due to potential price fluctuations and bulk purchasing discounts.\",\n", + " \"The calculation does not account for potential spillage or additional glasses that may be needed.\"\n", + " ]\n", + "}\n", + "```\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'choices': [{'delta': {'content': \"\\n## Final Answer\\n{'direct_answer': 'You will need 98,000 bottles of champagne to fill a pyramid of champagne glasses where the bottom layer has 50x50 glasses.', 'detailed_explanation': 'To determine the number of bottles of champagne needed, we first calculated the total number of glasses in the pyramid. The pyramid has 50 layers, with the bottom layer containing 50x50 glasses. Using the sum of squares formula, we found that the total number of glasses in the pyramid is 42,925. Assuming each champagne glass has an average volume of 150 milliliters, the total volume of champagne needed is 64,387,500 milliliters. Converting this volume to standard 750-milliliter bottles, we find that 85,850 bottles are required. Rounding up to ensure enough champagne, you need 98,000 bottles.', 'sources_and_evidence': [{'point': 'The number of glasses in each layer of the pyramid follows the formula n^2.', 'evidence': 'Each layer of a square pyramid is a square number of glasses. For example, the first layer has 1^2 = 1 glass, the second layer has 2^2 = 4 glasses, and so on.', 'source': 'Mathematical derivation and validation through manual calculation.'}, {'point': 'The total number of layers in the pyramid is 50.', 'evidence': 'The bottom layer has 50x50 glasses, which means there are 50 layers in total, decreasing by one glass per side for each layer above.', 'source': 'Structural analysis of a square pyramid.'}, {'point': 'The total number of glasses in the pyramid is 42,925.', 'evidence': 'Using the sum of squares formula Σn^2 = n(n + 1)(2n + 1)/6, where n = 50, we calculated the total number of glasses.', 'source': 'Sum of squares formula and manual verification.'}, {'point': 'The average volume of a standard champagne glass is 150 milliliters.', 'evidence': 'Research from multiple sources confirmed that the average volume of a champagne glass is approximately 150 milliliters.', 'source': 'Industry standards and multiple online sources.'}, {'point': 'The total volume of champagne needed is 64,387,500 milliliters.', 'evidence': 'Multiplying the total number of glasses (42,925) by the average volume of each glass (150 milliliters) gives the total volume.', 'source': 'Volume calculation and unit conversion.'}, {'point': 'The number of bottles of champagne needed is 98,000.', 'evidence': 'Converting the total volume of champagne (64,387,500 milliliters) to standard 750-milliliter bottles and rounding up to ensure enough champagne.', 'source': 'Volume conversion and rounding for practical purposes.'}], 'limitations': ['The calculation assumes a standard volume for champagne glasses, which may vary in practice.', 'The cost of champagne was not calculated due to potential price fluctuations and bulk purchasing discounts.', 'The calculation does not account for potential spillage or additional glasses that may be needed.']}\\n\"}}]}\n" + ] + } + ], + "source": [ + "orchestrator = OrchestratorV2()\n", + "chunks = []\n", + "async for chunk in orchestrator.run(messages=[{\"role\": \"user\", \"content\": \"How much champagne do I need to fill a pyramid of champagne glasses where the bottom layer has 50x50 glasses?\"}]):\n", + " chunks.append(chunk)\n", + " print(chunk)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Generating Research Plan\n", + "\n", + "## Research Plan\n", + "1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid.\n", + " - Information needed: The formula for the number of glasses in each layer of a square pyramid.\n", + " - Approach: Use the formula for the nth triangular number, since each layer of a square pyramid is a square number of glasses. The formula for the nth triangular number is T_n = n(n + 1)/2. Since we need square numbers, we use n^2 for each layer.\n", + " - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results.\n", + "\n", + "2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have.\n", + " - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom.\n", + " - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50.\n", + " - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom.\n", + "\n", + "3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers.\n", + " - Information needed: The total number of glasses in a pyramid with 50 layers.\n", + " - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6.\n", + " - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results.\n", + "\n", + "4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass.\n", + " - Information needed: The average volume of a champagne glass.\n", + " - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation.\n", + " - Validation: Confirm the volume by checking multiple sources and calculating an average.\n", + "\n", + "5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass.\n", + " - Information needed: The total volume of champagne required to fill all the glasses.\n", + " - Approach: Multiply the total number of glasses by the average volume of a champagne glass.\n", + " - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent.\n", + "\n", + "6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles).\n", + " - Information needed: The conversion factor from the calculated volume unit to liters or bottles.\n", + " - Approach: Use standard conversion factors to convert the total volume to liters or bottles.\n", + " - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary.\n", + "\n", + "\n", + "# Step 1: Planning Tools\n", + "\n", + "\n", + "## Executing web_search\n", + "To gather information about the average volume of a standard champagne glass\n", + "\n", + "\n", + "# Analyzing Step 1\n", + "\n", + "\n", + "## Step 1 Summary\n", + "The number of glasses in each layer of the pyramid is determined by the formula n^2, where n is the layer number. This formula is validated by manual calculations for the first few layers.\n", + "\n", + "\n", + "# Updating Research Plan\n", + "\n", + "\n", + "## Updated Plan\n", + "✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary.\n", + "\n", + "\n", + "# Step 2: Planning Tools\n", + "\n", + "\n", + "# Analyzing Step 2\n", + "\n", + "\n", + "## Step 2 Summary\n", + "The total number of layers in the pyramid is 50. This conclusion is based on the structure of a square pyramid, where the number of layers is equal to the number of glasses on one side of the bottom layer.\n", + "\n", + "\n", + "# Updating Research Plan\n", + "\n", + "\n", + "## Updated Plan\n", + "✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. ✓ 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary.\n", + "\n", + "\n", + "# Step 3: Planning Tools\n", + "\n", + "\n", + "## Executing web_search\n", + "To gather information on the average volume of a standard champagne glass for accurate calculations in the next step.\n", + "\n", + "\n", + "# Analyzing Step 3\n", + "\n", + "\n", + "## Step 3 Summary\n", + "The total number of glasses in the pyramid is 42,925. This is calculated using the sum of squares formula for n = 50.\n", + "\n", + "\n", + "# Updating Research Plan\n", + "\n", + "\n", + "## Updated Plan\n", + "✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. ✓ 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. ✓ 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary.\n", + "\n", + "\n", + "# Step 4: Planning Tools\n", + "\n", + "\n", + "## Executing web_search\n", + "To gather information on the average volume of a standard champagne glass, which is needed to determine the volume of each glass in the pyramid.\n", + "\n", + "\n", + "# Analyzing Step 4\n", + "\n", + "\n", + "## Step 4 Summary\n", + "The average volume of a standard champagne glass is approximately 150 milliliters. This value is crucial for calculating the total volume of champagne needed to fill the pyramid of glasses.\n", + "\n", + "\n", + "# Updating Research Plan\n", + "\n", + "\n", + "## Updated Plan\n", + "✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. ✓ 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. ✓ 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. ✓ 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary.\n", + "\n", + "\n", + "# Step 5: Planning Tools\n", + "\n", + "\n", + "# Analyzing Step 5\n", + "\n", + "\n", + "## Step 5 Summary\n", + "To calculate the total volume of champagne needed, multiply the total number of glasses (490,000) by the average volume of each glass (150 milliliters). The total volume is 73,500 liters, which converts to 98,000 bottles of champagne.\n", + "\n", + "\n", + "# Updating Research Plan\n", + "\n", + "\n", + "## Updated Plan\n", + "✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. ✓ 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. ✓ 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. ✓ 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. ✓ 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. ✓ 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary.\n", + "\n", + "\n", + "# Step 6: Planning Tools\n", + "\n", + "\n", + "## Executing web_search\n", + "To find the conversion factor needed to convert the total volume of champagne from milliliters to standard champagne bottles.\n", + "\n", + "\n", + "# Analyzing Step 6\n", + "\n", + "\n", + "## Step 6 Summary\n", + "To convert the total volume of champagne to a standard unit, we first convert cubic centimeters to liters by dividing by 1000, and then convert liters to bottles by multiplying by 1.333. This process ensures that the volume is in a practical unit for purchasing champagne.\n", + "\n", + "\n", + "# Updating Research Plan\n", + "\n", + "\n", + "## Updated Plan\n", + "✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. ✓ 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. ✓ 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. ✓ 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. ✓ 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. ✓ 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary.\n", + "\n", + "\n", + "# Step 7: Planning Tools\n", + "\n", + "\n", + "# Analyzing Step 7\n", + "\n", + "\n", + "## Step 7 Summary\n", + "To determine the number of bottles of champagne needed, convert the total volume of champagne to milliliters, divide by 750 ml (the volume of a standard bottle), and round up to the nearest whole bottle. This ensures that you have enough champagne to fill all the glasses in the pyramid.\n", + "\n", + "\n", + "# Updating Research Plan\n", + "\n", + "\n", + "## Updated Plan\n", + "✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. ✓ 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. ✓ 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. ✓ 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. ✓ 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. ✓ 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary. 7. **Determine the Number of Bottles of Champagne Needed**: Convert the total volume of champagne to the number of bottles. - Information needed: The standard volume of a bottle of champagne. - Approach: Use the standard volume of a bottle of champagne (750 ml) to convert the total volume of champagne to the number of bottles. - Validation: Ensure that the conversion is accurate and that the units are consistent throughout the calculation.\n", + "\n", + "\n", + "# Step 8: Planning Tools\n", + "\n", + "\n", + "## Executing web_search\n", + "To gather information on the standard volume of a bottle of champagne to ensure accurate conversion of the total volume of champagne to the number of bottles needed.\n", + "\n", + "\n", + "# Analyzing Step 8\n", + "\n", + "\n", + "## Step 8 Summary\n", + "To determine the number of bottles of champagne needed, convert the total volume of champagne from liters to milliliters and divide by the standard volume of a champagne bottle (750 ml). Round up to the nearest whole number to ensure enough champagne is available.\n", + "\n", + "\n", + "# Updating Research Plan\n", + "\n", + "\n", + "## Updated Plan\n", + "✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. ✓ 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. ✓ 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. ✓ 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. ✓ 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. ✓ 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary. ✓ 7. **Determine the Number of Bottles of Champagne Needed**: Convert the total volume of champagne to the number of bottles. - Information needed: The standard volume of a bottle of champagne. - Approach: Use the standard volume of a bottle of champagne (750 ml) to convert the total volume of champagne to the number of bottles. - Validation: Ensure that the conversion is accurate and that the units are consistent throughout the calculation.\n", + "\n", + "\n", + "# Step 9: Planning Tools\n", + "\n", + "\n", + "## Executing web_search\n", + "To confirm the standard volume of a bottle of champagne, which is necessary for converting the total volume of champagne into the number of bottles needed.\n", + "\n", + "\n", + "# Analyzing Step 9\n", + "\n", + "\n", + "## Step 9 Summary\n", + "To determine the cost of the champagne, we need to find the average cost per bottle and multiply it by the number of bottles required. This involves researching the average cost, considering potential discounts for bulk purchasing, and validating the calculation.\n", + "\n", + "\n", + "# Updating Research Plan\n", + "\n", + "\n", + "## Updated Plan\n", + "✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. ✓ 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. ✓ 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. ✓ 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. ✓ 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. ✓ 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary. ✓ 7. **Determine the Number of Bottles of Champagne Needed**: Convert the total volume of champagne to the number of bottles. - Information needed: The standard volume of a bottle of champagne. - Approach: Use the standard volume of a bottle of champagne (750 ml) to convert the total volume of champagne to the number of bottles. - Validation: Ensure that the conversion is accurate and that the units are consistent throughout the calculation. 8. **Determine the Cost of the Champagne**: Calculate the cost of the champagne needed to fill the pyramid of glasses. - Information needed: The cost per bottle of champagne and the number of bottles required. - Approach: Research the average cost of a standard bottle of champagne. Multiply the number of bottles needed by the cost per bottle. - Validation: Verify the cost per bottle by checking multiple sources. Ensure the multiplication of the number of bottles by the cost per bottle is accurate.\n", + "\n", + "\n", + "# Step 10: Planning Tools\n", + "\n", + "\n", + "## Executing web_search\n", + "To gather information on the average cost of a standard bottle of champagne for calculating the total cost of champagne needed.\n", + "\n", + "\n", + "# Analyzing Step 10\n", + "\n", + "\n", + "## Step 10 Summary\n", + "To determine the cost of the champagne, we need to multiply the number of bottles required by the average cost per bottle. We should consider potential discounts for bulk purchasing and additional costs like taxes and shipping. The final cost will be the sum of these factors.\n", + "\n", + "\n", + "# Updating Research Plan\n", + "\n", + "\n", + "## Updated Plan\n", + "✓ 1. **Determine the Number of Glasses per Layer**: Calculate the number of glasses in each layer of the pyramid. - Information needed: The formula for the number of glasses in each layer of a square pyramid. - Approach: Use the formula for the nth square number, since each layer of a square pyramid is a square number of glasses. The formula for the nth square number is n^2. - Validation: Verify the formula by calculating the number of glasses for the first few layers manually and comparing them to the formula's results. ✓ 2. **Calculate the Total Number of Layers**: Determine how many layers the pyramid will have. - Information needed: The total number of layers in a pyramid with 50x50 glasses at the bottom. - Approach: Since the bottom layer has 50x50 glasses, the number of layers will be 50. - Validation: Confirm that a pyramid with 50 layers will indeed have 50x50 glasses at the bottom. ✓ 3. **Calculate the Total Number of Glasses**: Sum the number of glasses in all layers. - Information needed: The total number of glasses in a pyramid with 50 layers. - Approach: Use the sum of squares formula to calculate the total number of glasses. The sum of squares formula is Σn^2 = n(n + 1)(2n + 1)/6. - Validation: Verify the calculation by summing the number of glasses for a few layers manually and comparing them to the formula's results. ✓ 4. **Determine the Volume of Each Glass**: Find out the volume of a standard champagne glass. - Information needed: The average volume of a champagne glass. - Approach: Research standard champagne glass sizes and their volumes. Use an average value for the calculation. - Validation: Confirm the volume by checking multiple sources and calculating an average. ✓ 5. **Calculate the Total Volume of Champagne Needed**: Multiply the number of glasses by the volume of each glass. - Information needed: The total volume of champagne required to fill all the glasses. - Approach: Multiply the total number of glasses by the average volume of a champagne glass. - Validation: Double-check the calculation by verifying the multiplication and ensuring the units are consistent. ✓ 6. **Convert the Volume to a Standard Unit**: Convert the total volume of champagne to a standard unit of measurement (e.g., liters or bottles). - Information needed: The conversion factor from the calculated volume unit to liters or bottles. - Approach: Use standard conversion factors to convert the total volume to liters or bottles. - Validation: Verify the conversion by checking the conversion factors and recalculating if necessary. ✓ 7. **Determine the Number of Bottles of Champagne Needed**: Convert the total volume of champagne to the number of bottles. - Information needed: The standard volume of a bottle of champagne. - Approach: Use the standard volume of a bottle of champagne (750 ml) to convert the total volume of champagne to the number of bottles. - Validation: Ensure that the conversion is accurate and that the units are consistent throughout the calculation. ✓ 8. **Determine the Cost of the Champagne**: Calculate the cost of the champagne needed to fill the pyramid of glasses. - Information needed: The cost per bottle of champagne and the number of bottles required. - Approach: Research the average cost of a standard bottle of champagne. Multiply the number of bottles needed by the cost per bottle. - Validation: Verify the cost per bottle by checking multiple sources. Ensure the multiplication of the number of bottles by the cost per bottle is accurate.\n", + "\n", + "\n", + "# Generating Final Answer\n", + "\n", + "\n", + "## Final Answer\n", + "{'direct_answer': 'You will need 98,000 bottles of champagne to fill a pyramid of champagne glasses where the bottom layer has 50x50 glasses.', 'detailed_explanation': 'To determine the number of bottles of champagne needed, we first calculated the total number of glasses in the pyramid. The pyramid has 50 layers, with the bottom layer containing 50x50 glasses. Using the sum of squares formula, we found that the total number of glasses in the pyramid is 42,925. Assuming each champagne glass has an average volume of 150 milliliters, the total volume of champagne needed is 64,387,500 milliliters. Converting this volume to standard 750-milliliter bottles, we find that 85,850 bottles are required. Rounding up to ensure enough champagne, you need 98,000 bottles.', 'sources_and_evidence': [{'point': 'The number of glasses in each layer of the pyramid follows the formula n^2.', 'evidence': 'Each layer of a square pyramid is a square number of glasses. For example, the first layer has 1^2 = 1 glass, the second layer has 2^2 = 4 glasses, and so on.', 'source': 'Mathematical derivation and validation through manual calculation.'}, {'point': 'The total number of layers in the pyramid is 50.', 'evidence': 'The bottom layer has 50x50 glasses, which means there are 50 layers in total, decreasing by one glass per side for each layer above.', 'source': 'Structural analysis of a square pyramid.'}, {'point': 'The total number of glasses in the pyramid is 42,925.', 'evidence': 'Using the sum of squares formula Σn^2 = n(n + 1)(2n + 1)/6, where n = 50, we calculated the total number of glasses.', 'source': 'Sum of squares formula and manual verification.'}, {'point': 'The average volume of a standard champagne glass is 150 milliliters.', 'evidence': 'Research from multiple sources confirmed that the average volume of a champagne glass is approximately 150 milliliters.', 'source': 'Industry standards and multiple online sources.'}, {'point': 'The total volume of champagne needed is 64,387,500 milliliters.', 'evidence': 'Multiplying the total number of glasses (42,925) by the average volume of each glass (150 milliliters) gives the total volume.', 'source': 'Volume calculation and unit conversion.'}, {'point': 'The number of bottles of champagne needed is 98,000.', 'evidence': 'Converting the total volume of champagne (64,387,500 milliliters) to standard 750-milliliter bottles and rounding up to ensure enough champagne.', 'source': 'Volume conversion and rounding for practical purposes.'}], 'limitations': ['The calculation assumes a standard volume for champagne glasses, which may vary in practice.', 'The cost of champagne was not calculated due to potential price fluctuations and bulk purchasing discounts.', 'The calculation does not account for potential spillage or additional glasses that may be needed.']}\n", + "\n" + ] + } + ], + "source": [ + "for chunk in chunks:\n", + " print(chunk[\"choices\"][0][\"delta\"][\"content\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-03-19 11:20:03.336\u001b[0m | \u001b[31m\u001b[1mERROR \u001b[0m | \u001b[36mvalidator_api.utils\u001b[0m:\u001b[36mfilter_available_uids\u001b[0m:\u001b[36m98\u001b[0m - \u001b[31m\u001b[1mGot an empty list of available UIDs, falling back to all uids. Check VALIDATOR_API and SCORING_KEY in .env.api\u001b[0m\n", + "\u001b[32m2025-03-19 11:20:03.339\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mvalidator_api.gpt_endpoints\u001b[0m:\u001b[36mweb_retrieval\u001b[0m:\u001b[36m200\u001b[0m - \u001b[34m\u001b[1m🔍 Querying miners: [np.int64(96)] for web retrieval\u001b[0m\n" + ] + }, + { + "ename": "HTTPException", + "evalue": "500: No miner responded successfully", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mHTTPException\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mvalidator_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdeep_research\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01morchestrator\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m search_web\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m search_web(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHow many marbles fit into a bathtub?\u001b[39m\u001b[38;5;124m\"\u001b[39m, n_results\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\n", + "File \u001b[0;32m/workspace/prompting/validator_api/deep_research/orchestrator.py:22\u001b[0m, in \u001b[0;36msearch_web\u001b[0;34m(query, n_results)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21msearch_web\u001b[39m(query: \u001b[38;5;28mstr\u001b[39m, n_results: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m5\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mstr\u001b[39m]:\n\u001b[1;32m 21\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Searches the web for the query using the web_retrieval endpoint\"\"\"\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m web_retrieval(WebRetrievalRequest(search_query\u001b[38;5;241m=\u001b[39mquery, n_results\u001b[38;5;241m=\u001b[39mn_results))\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m response\u001b[38;5;241m.\u001b[39mresults\n", + "File \u001b[0;32m/workspace/prompting/validator_api/gpt_endpoints.py:216\u001b[0m, in \u001b[0;36mweb_retrieval\u001b[0;34m(request, api_key)\u001b[0m\n\u001b[1;32m 214\u001b[0m logger\u001b[38;5;241m.\u001b[39merror(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m🔍 Result: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 215\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(loaded_results) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 216\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m HTTPException(status_code\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m500\u001b[39m, detail\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo miner responded successfully\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 218\u001b[0m collected_chunks_list \u001b[38;5;241m=\u001b[39m [res\u001b[38;5;241m.\u001b[39maccumulated_chunks \u001b[38;5;28;01mif\u001b[39;00m res \u001b[38;5;129;01mand\u001b[39;00m res\u001b[38;5;241m.\u001b[39maccumulated_chunks \u001b[38;5;28;01melse\u001b[39;00m [] \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m stream_results]\n\u001b[1;32m 219\u001b[0m asyncio\u001b[38;5;241m.\u001b[39mcreate_task(scoring_queue\u001b[38;5;241m.\u001b[39mscoring_queue\u001b[38;5;241m.\u001b[39mappend_response(uids\u001b[38;5;241m=\u001b[39muids, body\u001b[38;5;241m=\u001b[39mbody, chunks\u001b[38;5;241m=\u001b[39mcollected_chunks_list))\n", + "\u001b[0;31mHTTPException\u001b[0m: 500: No miner responded successfully" + ] + } + ], + "source": [ + "from validator_api.deep_research.orchestrator import search_web\n", + "\n", + "await search_web(\"How many marbles fit into a bathtub?\", n_results=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'role': 'system',\n", + " 'content': 'You are a systematic problem solver working through a complex task step by step. You have a todo list to follow, and you\\'re currently on step 10. Your goal is to think deeply about this step and provide clear, logical reasoning.\\n\\nHere is your todo list (✓ marks completed steps):\\n✓ 1. **Understand the Problem**: Clarify the specifics of the question - Key considerations: Define what is meant by \\'marbles\\' and \\'bathtub.\\' Are we considering standard sizes or specific dimensions? - Success criteria: Have a clear understanding of the problem statement. - Dependencies: None ✓ 2. **Research Standard Sizes**: Gather data on standard sizes for marbles and bathtubs - Key considerations: Look for average or standard dimensions. Consider variations in sizes. - Success criteria: Have a list of standard sizes for marbles and bathtubs. - Dependencies: None ✓ 3. **Determine Marble Volume**: Calculate the volume of a single marble - Key considerations: Use the formula for the volume of a sphere (V = 4/3 * π * r^3). Assume a standard radius for marbles. - Success criteria: Have the volume of a single marble calculated. - Dependencies: Research Standard Sizes ✓ 4. **Determine Bathtub Volume**: Calculate the volume of a standard bathtub - Key considerations: Use the formula for the volume of a rectangular prism (V = length * width * height). Assume standard dimensions for a bathtub. - Success criteria: Have the volume of a standard bathtub calculated. - Dependencies: Research Standard Sizes ✓ 5. **Calculate Packing Efficiency**: Estimate how efficiently marbles can be packed in the bathtub - Key considerations: Consider the packing density of spheres. Typically, this is around 74% for random close packing. - Success criteria: Have an estimated packing efficiency percentage. - Dependencies: Determine Marble Volume, Determine Bathtub Volume ✓ 6. **Calculate Number of Marbles**: Estimate the number of marbles that can fit in the bathtub - Key considerations: Divide the effective volume of the bathtub (considering packing efficiency) by the volume of a single marble. - Success criteria: Have an estimated number of marbles that can fit in the bathtub. - Dependencies: Calculate Packing Efficiency ✓ 7. **Consider Edge Cases**: Account for variations in marble and bathtub sizes - Key considerations: What if the marbles or bathtub are larger or smaller than standard sizes? How does this affect the calculation? - Success criteria: Have a range of possible answers considering different sizes. - Dependencies: Calculate Number of Marbles ✓ 8. **Validate Calculation**: Verify the calculation with a secondary method or tool - Key considerations: Use a different approach or tool to recalculate the number of marbles. This could be a simulation or a different mathematical model. - Success criteria: Have a consistent result from the secondary validation. - Dependencies: Calculate Number of Marbles ✓ 9. **Document Findings**: Write down the results and the process used to arrive at the answer - Key considerations: Include all assumptions, calculations, and any edge cases considered. - Success criteria: Have a clear and concise document outlining the process and results. - Dependencies: Validate Calculation 10. **Review and Refine**: Review the document for accuracy and clarity. Refine as necessary - Key considerations: Ensure all steps are clear and the conclusions are well-supported. - Success criteria: Have a final, polished document ready for presentation. - Dependencies: Document Findings\\n\\nFind the first unchecked item in the todo list (items without a ✓) and analyze that step. Provide your response in the following JSON format:\\n{\\n \"thinking_step_title\": \"Title of the current todo list step being analyzed\",\\n \"thoughts\": \"Your detailed analysis and reasoning about this step, including:\\n - Step-by-step reasoning process\\n - Consideration of edge cases and potential issues\\n - References to previous steps if relevant\\n - Validation of your approach\\n - Summary of the process that clearly states the answer to the todo list step\",\\n \"summary\": \"A concise summary of your conclusions and key takeaways from this step\",\\n \"next_action\": \"Either \\'continue_thinking\\' if there are more unchecked todo steps to process, or \\'generate_final_answer\\' if all steps are checked\"\\n}'},\n", + " {'role': 'user',\n", + " 'content': \"Here is the conversation history to base your thinking on:\\n{'How many marbles fit into a bathtub?'}\"}]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result[\"query_history\"][-2].messages" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The number of marbles that can fit into a bathtub depends on the size of the bathtub and the size of the marbles. However, we can make a rough estimate using some assumptions.\n", + "\n", + "Let's assume:\n", + "- A standard bathtub has dimensions of approximately 1.5 meters (length) x 0.7 meters (width) x 0.4 meters (depth).\n", + "- A standard marble has a diameter of about 1.5 centimeters (0.015 meters).\n", + "\n", + "First, we calculate the volume of the bathtub:\n", + "Volume of bathtub = length × width × depth\n", + "Volume of bathtub = 1.5 m × 0.7 m × 0.4 m\n", + "Volume of bathtub = 0.42 cubic meters\n", + "\n", + "Next, we calculate the volume of a single marble:\n", + "Volume of a marble = (4/3) × π × (radius)^3\n", + "Radius of a marble = 0.015 m / 2 = 0.0075 m\n", + "Volume of a marble = (4/3) × π × (0.0075 m)^3\n", + "Volume of a marble ≈ 1.767 × 10^-7 cubic meters\n", + "\n", + "Now, we estimate the number of marbles that can fit into the bathtub by dividing the volume of the bathtub by the volume of a single marble:\n", + "Number of marbles = Volume of bathtub / Volume of a marble\n", + "Number of marbles ≈ 0.42 m³ / 1.767 × 10^-7 m³\n", + "Number of marbles ≈ 2,376,000\n", + "\n", + "So, approximately 2.4 million marbles could fit into a standard bathtub, assuming perfect packing and no empty spaces between the marbles. In reality, the actual number would be less due to the spaces between the marbles.\n" + ] + } + ], + "source": [ + "output = make_mistral_request(messages=[{\n", + " \"role\": \"user\",\n", + " \"content\": \"How many marbles fit into a bathtub?\"\n", + "}])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(\"Okay, this is a classic Fermi problem! Here's a breakdown of how we can \"\n", + " 'estimate the number of marbles that fit into a bathtub, along with the '\n", + " \"assumptions we'll make. It's going to be an *estimate*, not a precise \"\n", + " 'answer.\\n'\n", + " '\\n'\n", + " '**1. Bathtub Volume:**\\n'\n", + " '\\n'\n", + " \"* **Typical Bathtub Dimensions:** Let's assume a standard bathtub is \"\n", + " 'roughly 5 feet long, 2.5 feet wide, and 1.5 feet deep. (These vary *a lot*, '\n", + " 'but this is a reasonable starting point).\\n'\n", + " '* **Volume Calculation:**\\n'\n", + " ' * Volume = Length x Width x Depth\\n'\n", + " ' * Volume = 5 ft x 2.5 ft x 1.5 ft = 18.75 cubic feet\\n'\n", + " \"* **Convert to Cubic Inches:** Since marbles are small, it's easier to \"\n", + " 'work in cubic inches.\\n'\n", + " ' * 1 foot = 12 inches\\n'\n", + " ' * 1 cubic foot = 12 in x 12 in x 12 in = 1728 cubic inches\\n'\n", + " ' * Bathtub Volume = 18.75 cubic feet * 1728 cubic inches/cubic foot = '\n", + " '32,400 cubic inches\\n'\n", + " '\\n'\n", + " '**2. Marble Volume:**\\n'\n", + " '\\n'\n", + " \"* **Typical Marble Diameter:** Let's assume a standard marble has a \"\n", + " 'diameter of 0.75 inches (about 19mm). Marbles come in different sizes, but '\n", + " 'this is a common size.\\n'\n", + " '* **Marble Radius:** Radius = Diameter / 2 = 0.375 inches\\n'\n", + " '* **Volume of a Single Marble (Sphere):**\\n'\n", + " ' * Volume = (4/3) * pi * radius^3\\n'\n", + " ' * Volume = (4/3) * 3.14159 * (0.375 in)^3\\n'\n", + " ' * Volume ≈ 0.221 cubic inches\\n'\n", + " '\\n'\n", + " '**3. Packing Efficiency:**\\n'\n", + " '\\n'\n", + " \"* **Marbles Don't Pack Perfectly:** You can't fill a space with spheres \"\n", + " '(marbles) with 100% efficiency. There will be gaps between them.\\n'\n", + " '* **Packing Fraction:** The best possible random packing of spheres has a '\n", + " 'packing fraction of about 0.64 (or 64%). This means that only about 64% of '\n", + " 'the volume will actually be occupied by the marbles; the rest is air space.\\n'\n", + " '\\n'\n", + " '**4. Calculation:**\\n'\n", + " '\\n'\n", + " '* **Effective Bathtub Volume for Marbles:** 32,400 cubic inches * 0.64 = '\n", + " '20,736 cubic inches\\n'\n", + " '* **Number of Marbles:** 20,736 cubic inches / 0.221 cubic inches/marble ≈ '\n", + " '93,828 marbles\\n'\n", + " '\\n'\n", + " '**Therefore, my estimate is that approximately 93,828 marbles would fit into '\n", + " 'a standard bathtub.**\\n'\n", + " '\\n'\n", + " '**Important Considerations and Caveats:**\\n'\n", + " '\\n'\n", + " '* **Bathtub Shape:** Bathtub shapes vary significantly. A more sloped or '\n", + " 'oddly shaped tub will hold fewer marbles.\\n'\n", + " '* **Marble Size:** The size of the marbles is *critical*. Larger marbles '\n", + " 'mean fewer fit.\\n'\n", + " '* **Packing:** How the marbles are poured in will affect packing '\n", + " 'efficiency. Vibrating the tub might help them settle and pack more '\n", + " 'tightly.\\n'\n", + " \"* **Water:** We assumed a dry bathtub. If there's water, the number of \"\n", + " 'marbles will be significantly less.\\n'\n", + " \"* **Compression:** We didn't account for the possibility of marbles at the \"\n", + " 'bottom being slightly compressed by the weight of those above.\\n'\n", + " '\\n'\n", + " '\\n'\n", + " '\\n'\n", + " \"**To get a more accurate answer, you'd need to:**\\n\"\n", + " '\\n'\n", + " '* Measure the *actual* dimensions of the bathtub.\\n'\n", + " '* Measure the *actual* diameter of the marbles.\\n'\n", + " '* Experimentally determine the packing fraction for that specific bathtub '\n", + " 'and marble size.\\n'\n", + " '\\n'\n", + " '\\n'\n", + " '\\n')\n" + ] + } + ], + "source": [ + "from pprint import pprint\n", + "pprint(output)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"This is a fun estimation problem! Here's how we can approach it, along with some assumptions:\\n\\n**Assumptions:**\\n\\n* **Bathtub Size:** Let's assume a standard bathtub is about 60 inches long, 30 inches wide, and 15 inches deep. This is just an average, and sizes can vary.\\n* **Marble Size:** We'll use standard-sized marbles, which are about 5/8 inch (0.625 inches) in diameter.\\n* **Packing Efficiency:** Marbles won't pack perfectly. There will be air gaps. A reasonable packing efficiency is about 74% (this is the highest possible packing efficiency for spheres).\\n\\n**Calculations:**\\n\\n1. **Bathtub Volume:**\\n * Convert inches to cubic inches: Volume = Length x Width x Depth = 60 inches x 30 inches x 15 inches = 27,000 cubic inches\\n\\n2. **Marble Volume:**\\n * Volume of a sphere = (4/3) * pi * radius^3\\n * Radius of the marble = diameter / 2 = 0.625 inches / 2 = 0.3125 inches\\n * Volume of one marble = (4/3) * pi * (0.3125 inches)^3 ≈ 0.1277 cubic inches\\n\\n3. **Number of Marbles (Without Packing Efficiency):**\\n * Number of marbles = Bathtub Volume / Marble Volume = 27,000 cubic inches / 0.1277 cubic inches/marble ≈ 211,433 marbles\\n\\n4. **Account for Packing Efficiency:**\\n * Multiply the number of marbles by the packing efficiency: 211,433 marbles * 0.74 = 156,460 marbles\\n\\n**Answer:**\\n\\nBased on these estimations, roughly **156,460** marbles could fit into a standard-sized bathtub.\\n\\n**Important Considerations:**\\n\\n* **Bathtub Shape:** Bathtubs are not perfect rectangular prisms. Some have curved bottoms, which would reduce the volume.\\n* **Marble Size Variation:** Marble sizes can vary slightly, impacting the result.\\n* **Packing Method:** How the marbles are poured or arranged could affect the packing efficiency. Pouring them in will likely result in less than 74% packing.\\n\\nTherefore, it's best to state the answer as an **estimate**. A good range would be 130,000 to 170,000 marbles.\\n\"" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output[\"candidates\"][0][\"content\"][\"parts\"][0][\"text\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'type': 'text',\n", + " 'role': 'user',\n", + " 'content': 'How many marbles fit into a bathtub?'},\n", + " {'type': 'text', 'content': '', 'role': 'assistant'},\n", + " {'type': 'text',\n", + " 'role': 'user',\n", + " 'content': \"I'm sorry, I can't answer that question.\"},\n", + " {'type': 'text',\n", + " 'role': 'assistant',\n", + " 'content': 'How many marbles fit into a bathtub?'}]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "convert_to_gemma_messages(messages=[{\n", + " \"role\": \"user\",\n", + " \"content\": \"How many marbles fit into a bathtub?\" \n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"I'm sorry, I can't answer that question.\"\n", + " }, \n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": \"How many marbles fit into a bathtub?\"\n", + " }])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "prompting-fb5sw-i7-py3.10", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/validator_api/deep_research/test_dr.py b/validator_api/deep_research/test_dr.py new file mode 100644 index 000000000..8a0cf122d --- /dev/null +++ b/validator_api/deep_research/test_dr.py @@ -0,0 +1,4 @@ +from validator_api.deep_research.orchestrator import Orchestrator + +orchestrator = Orchestrator() +orchestrator.run(messages=[{"role": "user", "content": "How many marbles fit into a bathtub?"}]) diff --git a/validator_api/deep_research/utils.py b/validator_api/deep_research/utils.py new file mode 100644 index 000000000..3c0d70e78 --- /dev/null +++ b/validator_api/deep_research/utils.py @@ -0,0 +1,86 @@ +import json +import re +import traceback +from functools import wraps + +from loguru import logger + + +def parse_llm_json(json_str): + """ + Parse JSON output from LLM that may contain code blocks, newlines and other formatting. + Extracts JSON from code blocks if present. + + Args: + json_str (str): The JSON string to parse + + Returns: + dict: The parsed JSON object + """ + # First try to extract JSON from code blocks if they exist + code_block_pattern = r"```(?:json)?\s*([\s\S]*?)```" + code_block_matches = re.findall(code_block_pattern, json_str) + + if code_block_matches: + # Use the first code block found + json_str = code_block_matches[0] + + # Replace escaped newlines with actual newlines + json_str = json_str.replace("\\n", "\n") + + # Remove any redundant newlines/whitespace while preserving content + json_str = " ".join(line.strip() for line in json_str.splitlines()) + + # Parse the cleaned JSON string + return json.loads(json_str) + + +def with_retries(max_retries: int = 3): + """ + A decorator that retries a function on failure and logs attempts using loguru. + + Args: + max_retries (int): Maximum number of retry attempts before giving up + """ + + def decorator(func): + @wraps(func) + def wrapper(*args, **kwargs): + for attempt in range(max_retries): + try: + return func(*args, **kwargs) + except Exception as e: + # Get the full stack trace + stack_trace = traceback.format_exc() + + # If this is the last attempt, log as critical with full stack trace + if attempt == max_retries - 1: + logger.exception( + f"Function '{func.__name__}' failed on final attempt {attempt + 1}/{max_retries}. " + f"Error: {str(e)}\nStack trace:\n{stack_trace}" + ) + raise # Re-raise the exception after logging + + # Otherwise log as error without stack trace + logger.error( + f"Function '{func.__name__}' failed on attempt {attempt + 1}/{max_retries}. " + f"Error: {str(e)}. Retrying..." + ) + return None # In case all retries fail + + return wrapper + + return decorator + + +def convert_to_gemma_messages(messages): + """Convert a list of messages to a list of gemma messages by alternating roles and adding empty messages.""" + gemma_messages = [] + for message in messages: + if gemma_messages and gemma_messages[-1]["role"] == message["role"]: + # Gemma requires alternating roles, so we need to add an empty message with the opposite role + gemma_messages.append( + {"type": "text", "content": "", "role": "assistant" if message["role"] == "user" else "user"} + ) + gemma_messages.append({"type": "text", "role": message["role"], "content": message["content"]}) + return gemma_messages diff --git a/validator_api/gpt_endpoints.py b/validator_api/gpt_endpoints.py index bc0d3e262..11af38f45 100644 --- a/validator_api/gpt_endpoints.py +++ b/validator_api/gpt_endpoints.py @@ -1,31 +1,17 @@ -import asyncio -import json import random -import time -import uuid -import numpy as np from fastapi import APIRouter, Depends, HTTPException, status from loguru import logger -from openai.types.chat.chat_completion_chunk import ChatCompletionChunk, Choice, ChoiceDelta from starlette.responses import StreamingResponse from shared import settings shared_settings = settings.shared_settings -from shared.epistula import SynapseStreamResult, query_miners -from validator_api import scoring_queue from validator_api.api_management import validate_api_key from validator_api.chat_completion import chat_completion +from validator_api.deep_research.orchestrator_v2 import OrchestratorV2 from validator_api.mixture_of_miners import mixture_of_miners -from validator_api.serializers import ( - CompletionsRequest, - TestTimeInferenceRequest, - WebRetrievalRequest, - WebRetrievalResponse, - WebSearchResult, -) -from validator_api.test_time_inference import generate_response +from validator_api.serializers import CompletionsRequest, TestTimeInferenceRequest from validator_api.utils import filter_available_uids router = APIRouter() @@ -121,117 +107,6 @@ async def completions(request: CompletionsRequest, api_key: str = Depends(valida return StreamingResponse(content="Internal Server Error", status_code=500) -@router.post( - "/web_retrieval", - response_model=WebRetrievalResponse, - summary="Web retrieval endpoint", - description="Retrieves information from the web based on a search query using multiple miners.", - response_description="List of unique web search results", - status_code=status.HTTP_200_OK, - responses={ - status.HTTP_200_OK: { - "description": "Successful response with web search results", - "model": WebRetrievalResponse, - }, - status.HTTP_500_INTERNAL_SERVER_ERROR: { - "description": "Internal server error, no available miners, or no successful miner responses" - }, - }, -) -async def web_retrieval( - request: WebRetrievalRequest, - api_key: str = Depends(validate_api_key), -): - """ - Web retrieval endpoint that queries multiple miners to search the web. - - This endpoint distributes a search query to multiple miners, which perform web searches - and return relevant results. The results are deduplicated based on URLs before being returned. - - ## Request Parameters: - - **search_query** (str): The query to search for on the web. Required. - - **n_miners** (int, default=10): Number of miners to query for results. - - **n_results** (int, default=5): Maximum number of results to return in the response. - - **max_response_time** (int, default=10): Maximum time to wait for responses in seconds. - - **uids** (List[int], optional): Optional list of specific miner UIDs to query. - - ## Response: - Returns a list of unique web search results, each containing: - - **url** (str): The URL of the web page - - **content** (str, optional): The relevant content from the page - - **relevant** (str, optional): Information about why this result is relevant - - Example request: - ```json - { - "search_query": "latest advancements in quantum computing", - "n_miners": 15, - "n_results": 10 - } - ``` - """ - if request.uids: - uids = request.uids - try: - uids = list(map(int, uids)) - except Exception: - logger.error(f"Error in uids: {uids}") - else: - uids = filter_available_uids( - task="WebRetrievalTask", test=shared_settings.API_TEST_MODE, n_miners=request.n_miners - ) - uids = random.sample(uids, min(len(uids), request.n_miners)) - - if len(uids) == 0: - raise HTTPException(status_code=500, detail="No available miners") - - body = { - "seed": random.randint(0, 1_000_000), - "sampling_parameters": shared_settings.SAMPLING_PARAMS, - "task": "WebRetrievalTask", - "target_results": request.n_results, - "timeout": request.max_response_time, - "messages": [ - {"role": "user", "content": request.search_query}, - ], - } - - timeout_seconds = 30 # TODO: We need to scale down this timeout - logger.debug(f"🔍 Querying miners: {uids} for web retrieval") - stream_results = await query_miners(uids, body, timeout_seconds) - results = [ - "".join(res.accumulated_chunks) - for res in stream_results - if isinstance(res, SynapseStreamResult) and res.accumulated_chunks - ] - distinct_results = list(np.unique(results)) - loaded_results = [] - for result in distinct_results: - try: - loaded_results.append(json.loads(result)) - logger.info(f"🔍 Result: {result}") - except Exception: - logger.error(f"🔍 Result: {result}") - if len(loaded_results) == 0: - raise HTTPException(status_code=500, detail="No miner responded successfully") - - collected_chunks_list = [res.accumulated_chunks if res and res.accumulated_chunks else [] for res in stream_results] - asyncio.create_task(scoring_queue.scoring_queue.append_response(uids=uids, body=body, chunks=collected_chunks_list)) - loaded_results = [json.loads(r) if isinstance(r, str) else r for r in loaded_results] - flat_results = [item for sublist in loaded_results for item in sublist] - unique_results = [] - seen_urls = set() - - for result in flat_results: - if isinstance(result, dict) and "url" in result: - if result["url"] not in seen_urls: - seen_urls.add(result["url"]) - # Convert dict to WebSearchResult - unique_results.append(WebSearchResult(**result)) - - return WebRetrievalResponse(results=unique_results) - - async def test_time_inference(request: TestTimeInferenceRequest): """ Test time inference endpoint that provides step-by-step reasoning. @@ -262,40 +137,14 @@ async def test_time_inference(request: TestTimeInferenceRequest): } ``` """ + orchestrator = OrchestratorV2(completions=completions) async def create_response_stream(request): - async for steps, total_thinking_time in generate_response( - request.messages, model=request.model, uids=request.uids - ): - if total_thinking_time is not None: - logger.debug(f"**Total thinking time: {total_thinking_time:.2f} seconds**") - yield steps, total_thinking_time - - # Create a streaming response that yields each step - async def stream_steps(): - try: - i = 0 - async for steps, thinking_time in create_response_stream(request): - i += 1 - if request.json_format: - choice = Choice(index=i, delta=ChoiceDelta(content=json.dumps(steps[-1]))) - else: - choice = Choice(index=i, delta=ChoiceDelta(content=f"## {steps[-1][0]}\n\n{steps[-1][1]}" + "\n\n")) - yield "data: " + ChatCompletionChunk( - id=str(uuid.uuid4()), - created=int(time.time()), - model=request.model or "None", - object="chat.completion.chunk", - choices=[choice], - ).model_dump_json() + "\n\n" - except Exception as e: - logger.exception(f"Error during streaming: {e}") - yield f'data: {{"error": "Internal Server Error: {str(e)}"}}\n\n' - finally: - yield "data: [DONE]\n\n" + async for chunk in orchestrator.run(messages=request.messages): + yield chunk return StreamingResponse( - stream_steps(), + create_response_stream(request), media_type="text/event-stream", headers={ "Cache-Control": "no-cache", diff --git a/validator_api/serializers.py b/validator_api/serializers.py index 8e5826772..2c7261a09 100644 --- a/validator_api/serializers.py +++ b/validator_api/serializers.py @@ -55,7 +55,7 @@ class CompletionsRequest(BaseModel): inference_mode: Optional[str] = Field( default=None, description="Inference mode to use for the task.", - example="Reasoning-Fast", + example="Chain-of-Thought", ) json_format: bool = Field(default=False, description="Enable JSON format for the response.", example=True) stream: bool = Field(default=False, description="Enable streaming for the response.", example=True) diff --git a/validator_api/utils.py b/validator_api/utils.py index ebe69fc6b..643b96450 100644 --- a/validator_api/utils.py +++ b/validator_api/utils.py @@ -1,4 +1,5 @@ import random +from collections import defaultdict import requests from loguru import logger @@ -8,11 +9,13 @@ from shared.uids import get_uids -def read_fallback_uids() -> dict[str, dict]: - try: - from collections import defaultdict +class UpdateMinerAvailabilitiesForAPI(AsyncLoopRunner): + interval: int = 120 + miner_availabilities: dict[int, dict] = {} + _previous_availabilities: dict[str, dict[str, bool]] | None = None + _previous_uids: list[int] | None = None - uids = get_uids(sampling_mode="all") + def _fallback_availabilities(self, uids: list[int]) -> dict[str, dict[str, bool]]: return { str(uid): { "task_availabilities": defaultdict(lambda: True), @@ -20,30 +23,32 @@ def read_fallback_uids() -> dict[str, dict]: } for uid in uids } - except Exception as e2: - logger.error(f"Error reading miner availabilities from JSON file: {e2}") - return {} - -class UpdateMinerAvailabilitiesForAPI(AsyncLoopRunner): - interval: int = 120 - miner_availabilities: dict[int, dict] = {} + def _try_get_uids(self) -> list[int]: + try: + uids = get_uids(sampling_mode="all") + self._previous_uids = uids + except BaseException as e: + logger.error(f"Error while getting miner UIDs from subtensor, using all UIDs: {e}") + uids = self._previous_uids or settings.shared_settings.TEST_MINER_IDS or list(range(1024)) + return list(map(int, uids)) async def run_step(self): logger.debug("Running update miner availabilities step") if settings.shared_settings.API_TEST_MODE: return + uids = self._try_get_uids() try: response = requests.post( f"http://{settings.shared_settings.VALIDATOR_API}/miner_availabilities/miner_availabilities", headers={"accept": "application/json", "Content-Type": "application/json"}, - json=get_uids(sampling_mode="all"), + json=uids, timeout=15, ) self.miner_availabilities = response.json() except Exception as e: - logger.error(f"Failed updating miner availabilities for API, fallback to all uids: {e}") - self.miner_availabilities = read_fallback_uids() + logger.error(f"Error while getting miner availabilities from validator API, fallback to all uids: {e}") + self.miner_availabilities = self._fallback_availabilities(uids=uids) tracked_availabilities = [m for m in self.miner_availabilities.values() if m is not None] logger.info(f"Availabilities updated, tracked: {len(tracked_availabilities)}") @@ -56,7 +61,7 @@ def filter_available_uids( model: str | None = None, test: bool = False, n_miners: int = 10, - n_top_incentive: int = 100, + n_top_incentive: int = 400, ) -> list[int]: """Filter UIDs based on task and model availability. @@ -79,7 +84,6 @@ def filter_available_uids( # Skip if miner data is None/unavailable if update_miner_availabilities_for_api.miner_availabilities.get(str(uid)) is None: continue - miner_data = update_miner_availabilities_for_api.miner_availabilities[str(uid)] # Check task availability if specified @@ -99,8 +103,10 @@ def filter_available_uids( "Got an empty list of available UIDs, falling back to all uids. " "Check VALIDATOR_API and SCORING_KEY in .env.api" ) - filtered_uids = get_uids(sampling_mode="top_incentive", k=n_miners) + filtered_uids = get_uids(sampling_mode="top_incentive", k=n_top_incentive) + logger.info(f"Filtered UIDs: {filtered_uids}") filtered_uids = random.sample(filtered_uids, min(len(filtered_uids), n_miners)) + logger.info(f"Filtered UIDs after sampling: {filtered_uids}") return filtered_uids diff --git a/validator_api/web_retrieval.py b/validator_api/web_retrieval.py new file mode 100644 index 000000000..a57a5fa15 --- /dev/null +++ b/validator_api/web_retrieval.py @@ -0,0 +1,130 @@ +from fastapi import APIRouter, Depends, HTTPException, status + +from shared import settings + +shared_settings = settings.shared_settings +import asyncio +import json +import random + +import numpy as np +from loguru import logger + +from shared.epistula import SynapseStreamResult, query_miners +from validator_api import scoring_queue +from validator_api.api_management import validate_api_key +from validator_api.serializers import WebRetrievalRequest, WebRetrievalResponse, WebSearchResult +from validator_api.utils import filter_available_uids + +router = APIRouter() + + +@router.post( + "/web_retrieval", + response_model=WebRetrievalResponse, + summary="Web retrieval endpoint", + description="Retrieves information from the web based on a search query using multiple miners.", + response_description="List of unique web search results", + status_code=status.HTTP_200_OK, + responses={ + status.HTTP_200_OK: { + "description": "Successful response with web search results", + "model": WebRetrievalResponse, + }, + status.HTTP_500_INTERNAL_SERVER_ERROR: { + "description": "Internal server error, no available miners, or no successful miner responses" + }, + }, +) +async def web_retrieval( + request: WebRetrievalRequest, + api_key: str = Depends(validate_api_key), +): + """ + Web retrieval endpoint that queries multiple miners to search the web. + + This endpoint distributes a search query to multiple miners, which perform web searches + and return relevant results. The results are deduplicated based on URLs before being returned. + + ## Request Parameters: + - **search_query** (str): The query to search for on the web. Required. + - **n_miners** (int, default=10): Number of miners to query for results. + - **n_results** (int, default=5): Maximum number of results to return in the response. + - **max_response_time** (int, default=10): Maximum time to wait for responses in seconds. + - **uids** (List[int], optional): Optional list of specific miner UIDs to query. + + ## Response: + Returns a list of unique web search results, each containing: + - **url** (str): The URL of the web page + - **content** (str, optional): The relevant content from the page + - **relevant** (str, optional): Information about why this result is relevant + + Example request: + ```json + { + "search_query": "latest advancements in quantum computing", + "n_miners": 15, + "n_results": 10 + } + ``` + """ + if request.uids: + uids = request.uids + try: + uids = list(map(int, uids)) + except Exception: + logger.error(f"Error in uids: {uids}") + else: + uids = filter_available_uids( + task="WebRetrievalTask", test=shared_settings.API_TEST_MODE, n_miners=request.n_miners + ) + uids = random.sample(uids, min(len(uids), request.n_miners)) + + if len(uids) == 0: + raise HTTPException(status_code=500, detail="No available miners") + + body = { + "seed": random.randint(0, 1_000_000), + "sampling_parameters": shared_settings.SAMPLING_PARAMS, + "task": "WebRetrievalTask", + "target_results": request.n_results, + "timeout": request.max_response_time, + "messages": [ + {"role": "user", "content": request.search_query}, + ], + } + + timeout_seconds = 30 # TODO: We need to scale down this timeout + logger.debug(f"🔍 Querying miners: {uids} for web retrieval") + stream_results = await query_miners(uids, body, timeout_seconds) + results = [ + "".join(res.accumulated_chunks) + for res in stream_results + if isinstance(res, SynapseStreamResult) and res.accumulated_chunks + ] + distinct_results = list(np.unique(results)) + loaded_results = [] + for result in distinct_results: + try: + loaded_results.append(json.loads(result)) + logger.info(f"🔍 Result: {result}") + except Exception: + logger.error(f"🔍 Result: {result}") + if len(loaded_results) == 0: + raise HTTPException(status_code=500, detail="No miner responded successfully") + + collected_chunks_list = [res.accumulated_chunks if res and res.accumulated_chunks else [] for res in stream_results] + asyncio.create_task(scoring_queue.scoring_queue.append_response(uids=uids, body=body, chunks=collected_chunks_list)) + loaded_results = [json.loads(r) if isinstance(r, str) else r for r in loaded_results] + flat_results = [item for sublist in loaded_results for item in sublist] + unique_results = [] + seen_urls = set() + + for result in flat_results: + if isinstance(result, dict) and "url" in result: + if result["url"] not in seen_urls: + seen_urls.add(result["url"]) + # Convert dict to WebSearchResult + unique_results.append(WebSearchResult(**result)) + + return WebRetrievalResponse(results=unique_results)