Berack96
148bff7cfd
- Added Predictor class with input preparation and instructions for financial strategy generation. - Removed PredictorAgent class and integrated its functionality into the new Predictor module. - Created a base market API wrapper and specific implementations for Coinbase and CryptoCompare. - Introduced PublicBinanceAgent for fetching public prices from Binance. - Refactored ToolAgent to utilize the new Predictor and market API wrappers for improved data handling and predictions. - Updated models to streamline the selection of available LLM providers. - Removed deprecated signer classes for Coinbase and CryptoCompare.
53 lines
2.2 KiB
Python
53 lines
2.2 KiB
Python
from app.agents.news_agent import NewsAgent
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from app.agents.social_agent import SocialAgent
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from app.agents import predictor
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from app.agents.predictor import PredictorStyle
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from app.markets import get_first_available_market_api
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from app.models import Models
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class ToolAgent:
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def __init__(self, available_models: list[Models], all_styles: list[PredictorStyle]):
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self.available_models = available_models
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self.all_styles = all_styles
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self.market = get_first_available_market_api(currency="USD")
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self.choose_provider(0) # Default to the first model
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def choose_provider(self, index: int):
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# TODO Utilizzare AGNO per gestire i modelli... è molto più semplice e permette di cambiare modello facilmente
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# TODO https://docs.agno.com/introduction
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# Inoltre permette di creare dei team e workflow di agenti più facilmente
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chosen_model = self.available_models[index]
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self.predictor = chosen_model.get_agent(predictor.instructions())
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self.news_agent = NewsAgent()
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self.social_agent = SocialAgent()
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def interact(self, query: str, style_index: int):
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"""
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Funzione principale che coordina gli agenti per rispondere alla richiesta dell'utente.
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"""
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# Step 1: raccolta analisi
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cryptos = ["BTC", "ETH", "XRP", "LTC", "BCH"] # TODO rendere dinamico in futuro
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market_data = self.market.get_products(cryptos)
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news_sentiment = self.news_agent.analyze(query)
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social_sentiment = self.social_agent.analyze(query)
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# Step 2: aggrega sentiment
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sentiment = f"{news_sentiment}\n{social_sentiment}"
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# Step 3: previsione
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inputs = predictor.prepare_inputs(
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data=market_data,
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style=self.all_styles[style_index],
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sentiment=sentiment
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)
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prediction = self.predictor.run(inputs)
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#output = prediction.content.split("</think>")[-1] # remove thinking steps and reasoning from the final output
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output = prediction.content
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market_data = "\n".join([f"{product.symbol}: {product.price}" for product in market_data])
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return f"{market_data}\n{sentiment}\n\n📈 Consiglio finale:\n{output}"
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