Refactor team management; asynchronous execution
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@@ -1,8 +1,5 @@
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from agno.run.agent import RunOutput
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from app.agents.models import AppModels
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from app.agents.team import create_team_with
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from app.agents.predictor import PREDICTOR_INSTRUCTIONS, PredictorInput, PredictorOutput, PredictorStyle
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from app.base.markets import ProductInfo
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from app.agents.predictor import PREDICTOR_INSTRUCTIONS, PredictorOutput, PredictorStyle
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class Pipeline:
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@@ -17,7 +14,6 @@ class Pipeline:
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self.all_styles = list(PredictorStyle)
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self.style = self.all_styles[0]
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self.team = create_team_with(AppModels.OLLAMA_QWEN_1B)
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self.choose_predictor(0) # Modello di default
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# ======================
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@@ -65,41 +61,10 @@ class Pipeline:
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4. Restituisce la strategia finale
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"""
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# Step 1: raccolta output dai membri del Team
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team_outputs = self.team.run(query) # type: ignore
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# Step 2: aggregazione output strutturati
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all_products: list[ProductInfo] = []
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sentiments: list[str] = []
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for agent_output in team_outputs.member_responses:
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if isinstance(agent_output, RunOutput) and agent_output.metadata is not None:
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keys = agent_output.metadata.keys()
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if "products" in keys:
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all_products.extend(agent_output.metadata["products"])
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if "sentiment_news" in keys:
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sentiments.append(agent_output.metadata["sentiment_news"])
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if "sentiment_social" in keys:
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sentiments.append(agent_output.metadata["sentiment_social"])
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aggregated_sentiment = "\n".join(sentiments)
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# Step 3: invocazione Predictor
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predictor_input = PredictorInput(
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data=all_products,
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style=self.style,
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sentiment=aggregated_sentiment
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)
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result = self.predictor.run(predictor_input) # type: ignore
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if not isinstance(result.content, PredictorOutput):
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return "❌ Errore: il modello non ha restituito un output valido."
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prediction: PredictorOutput = result.content
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# Step 4: restituzione strategia finale
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portfolio_lines = "\n".join(
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[f"{item.asset} ({item.percentage}%): {item.motivation}" for item in prediction.portfolio]
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)
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return (
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f"📊 Strategia ({self.style.value}): {prediction.strategy}\n\n"
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f"💼 Portafoglio consigliato:\n{portfolio_lines}"
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)
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from app.agents import AppTeam
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from agno.agent import RunEvent
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team = AppTeam(AppModels.OLLAMA_QWEN_1B) # TODO rendere dinamico
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team.add_listener(RunEvent.tool_call_started, lambda e: print(f"Team tool call started: {e.agent_name}")) # type: ignore
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team.add_listener(RunEvent.tool_call_completed, lambda e: print(f"Team tool call completed: {e.agent_name}")) # type: ignore
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result = team.run_team(query)
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return result
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@@ -1,33 +1,88 @@
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from agno.team import Team
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import asyncio
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import logging
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from typing import Callable, Self
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from agno.run.agent import RunOutputEvent
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from agno.team import Team, TeamRunEvent, TeamRunOutputEvent
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from agno.tools.reasoning import ReasoningTools
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from app.agents import AppModels
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from app.markets import MarketAPIsTool
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from app.news import NewsAPIsTool
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from app.social import SocialAPIsTool
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logging = logging.getLogger("AppTeam")
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def create_team_with(models: AppModels, coordinator: AppModels | None = None) -> Team:
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market_agent = models.get_agent(
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instructions=MARKET_INSTRUCTIONS,
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name="MarketAgent",
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tools=[MarketAPIsTool()]
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)
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news_agent = models.get_agent(
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instructions=NEWS_INSTRUCTIONS,
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name="NewsAgent",
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tools=[NewsAPIsTool()]
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)
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social_agent = models.get_agent(
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instructions=SOCIAL_INSTRUCTIONS,
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name="SocialAgent",
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tools=[SocialAPIsTool()]
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)
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coordinator = coordinator or models
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return Team(
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model=coordinator.get_model(COORDINATOR_INSTRUCTIONS),
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name="CryptoAnalysisTeam",
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members=[market_agent, news_agent, social_agent],
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)
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class AllTools:
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__instance: Self
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def __new__(cls) -> Self:
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if not hasattr(cls, "__instance"):
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cls.__instance = super(AllTools, cls).__new__(cls)
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return cls.__instance
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# TODO scegliere un modo migliore per inizializzare gli strumenti
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# TODO magari usare un config file o una classe apposta per i configs
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def __init__(self):
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self.market = MarketAPIsTool("EUR")
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self.news = NewsAPIsTool()
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self.social = SocialAPIsTool()
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class AppTeam:
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def __init__(self, team_models: AppModels, coordinator: AppModels | None = None):
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self.team_models = team_models
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self.coordinator = coordinator or team_models
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self.listeners: dict[str, Callable[[RunOutputEvent | TeamRunOutputEvent], None]] = {}
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def add_listener(self, event: str, listener: Callable[[RunOutputEvent | TeamRunOutputEvent], None]) -> None:
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self.listeners[event] = listener
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def run_team(self, query: str) -> str:
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return asyncio.run(self.run_team_async(query))
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async def run_team_async(self, query: str) -> str:
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logging.info(f"Running team q='{query}'")
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team = AppTeam.create_team_with(self.team_models, self.coordinator)
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result = "No output from team"
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async for run_event in team.arun(query, stream=True, stream_intermediate_steps=True): # type: ignore
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if run_event.event in self.listeners:
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self.listeners[run_event.event](run_event)
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if run_event.event in [TeamRunEvent.run_completed]:
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if isinstance(run_event.content, str):
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result = run_event.content
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thinking = result.rfind("</think>")
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if thinking != -1: result = result[thinking:]
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logging.info(f"Team finished")
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return result
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@staticmethod
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def create_team_with(models: AppModels, coordinator: AppModels) -> Team:
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tools = AllTools()
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market_agent = models.get_agent(
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instructions=MARKET_INSTRUCTIONS,
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name="MarketAgent",
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tools=[tools.market]
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)
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news_agent = models.get_agent(
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instructions=NEWS_INSTRUCTIONS,
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name="NewsAgent",
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tools=[tools.news]
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)
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social_agent = models.get_agent(
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instructions=SOCIAL_INSTRUCTIONS,
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name="SocialAgent",
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tools=[tools.social]
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)
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return Team(
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model=coordinator.get_model(COORDINATOR_INSTRUCTIONS),
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name="CryptoAnalysisTeam",
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members=[market_agent, news_agent, social_agent],
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tools=[ReasoningTools()]
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)
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COORDINATOR_INSTRUCTIONS = """
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You are the expert coordinator of a financial analysis team specializing in cryptocurrencies.
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