refactor: remove unused agent files and clean up market API instructions
This commit is contained in:
@@ -1,90 +0,0 @@
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from typing import Union, List, Dict, Optional, Any, Iterator, Sequence
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from agno.agent import Agent
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from agno.models.message import Message
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from agno.run.agent import RunOutput, RunOutputEvent
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from pydantic import BaseModel
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from app.toolkits.market_toolkit import MarketToolkit
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from app.markets.base import ProductInfo # modello dati già definito nel tuo progetto
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class MarketAgent(Agent):
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"""
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Wrapper che trasforma MarketToolkit in un Agent compatibile con Team.
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Produce sia output leggibile (content) che dati strutturati (metadata).
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"""
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def __init__(self, currency: str = "USD"):
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super().__init__()
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self.toolkit = MarketToolkit()
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self.currency = currency
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self.name = "MarketAgent"
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def run(
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self,
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input: Union[str, List, Dict, Message, BaseModel, List[Message]],
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*,
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stream: Optional[bool] = None,
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stream_intermediate_steps: Optional[bool] = None,
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user_id: Optional[str] = None,
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session_id: Optional[str] = None,
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session_state: Optional[Dict[str, Any]] = None,
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audio: Optional[Sequence[Any]] = None,
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images: Optional[Sequence[Any]] = None,
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videos: Optional[Sequence[Any]] = None,
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files: Optional[Sequence[Any]] = None,
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retries: Optional[int] = None,
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knowledge_filters: Optional[Dict[str, Any]] = None,
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add_history_to_context: Optional[bool] = None,
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add_dependencies_to_context: Optional[bool] = None,
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add_session_state_to_context: Optional[bool] = None,
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dependencies: Optional[Dict[str, Any]] = None,
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metadata: Optional[Dict[str, Any]] = None,
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yield_run_response: bool = False,
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debug_mode: Optional[bool] = None,
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**kwargs: Any,
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) -> Union[RunOutput, Iterator[Union[RunOutputEvent, RunOutput]]]:
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# 1. Estraggo la query dal parametro "input"
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if isinstance(input, str):
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query = input
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elif isinstance(input, dict) and "query" in input:
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query = input["query"]
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elif isinstance(input, Message):
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query = input.content
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elif isinstance(input, BaseModel):
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query = str(input)
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elif isinstance(input, list) and input and isinstance(input[0], Message):
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query = input[0].content
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else:
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query = str(input)
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# 2. Individuo i simboli da analizzare
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symbols = []
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for token in query.upper().split():
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if token in ("BTC", "ETH", "XRP", "LTC", "BCH"): # TODO: estendere dinamicamente
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symbols.append(token)
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if not symbols:
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symbols = ["BTC", "ETH"] # default
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# 3. Recupero i dati dal toolkit
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results = []
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products: List[ProductInfo] = []
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try:
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products.extend(self.toolkit.get_current_prices(symbols)) # supponiamo ritorni un ProductInfo o simile
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# Usa list comprehension per iterare symbols e products insieme
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results.extend([
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f"{symbol}: ${product.price:.2f}" if hasattr(product, 'price') and product.price else f"{symbol}: N/A"
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for symbol, product in zip(symbols, products)
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])
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except Exception as e:
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results.extend(f"Errore: Impossibile recuperare i dati di mercato\n{str(e)}")
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# 4. Preparo output leggibile + metadati strutturati
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output_text = "📊 Dati di mercato:\n" + "\n".join(results)
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return RunOutput(
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content=output_text,
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metadata={"products": products}
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)
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@@ -1,35 +0,0 @@
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from agno.agent import Agent
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class NewsAgent(Agent):
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"""
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Gli agenti devono esporre un metodo run con questa firma.
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def run(
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self,
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input: Union[str, List, Dict, Message, BaseModel, List[Message]],
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*,
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stream: Optional[bool] = None,
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stream_intermediate_steps: Optional[bool] = None,
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user_id: Optional[str] = None,
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session_id: Optional[str] = None,
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session_state: Optional[Dict[str, Any]] = None,
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audio: Optional[Sequence[Any]] = None,
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images: Optional[Sequence[Any]] = None,
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videos: Optional[Sequence[Any]] = None,
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files: Optional[Sequence[Any]] = None,
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retries: Optional[int] = None,
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knowledge_filters: Optional[Dict[str, Any]] = None,
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add_history_to_context: Optional[bool] = None,
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add_dependencies_to_context: Optional[bool] = None,
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add_session_state_to_context: Optional[bool] = None,
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dependencies: Optional[Dict[str, Any]] = None,
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metadata: Optional[Dict[str, Any]] = None,
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yield_run_response: bool = False,
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debug_mode: Optional[bool] = None,
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**kwargs: Any,
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) -> Union[RunOutput, Iterator[Union[RunOutputEvent, RunOutput]]]:
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"""
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@staticmethod
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def analyze(query: str) -> str:
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# Mock analisi news
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return "📰 Sentiment news: ottimismo sul mercato crypto grazie all'adozione istituzionale."
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@@ -1,36 +0,0 @@
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from agno.agent import Agent
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class SocialAgent(Agent):
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"""
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Gli agenti devono esporre un metodo run con questa firma.
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def run(
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self,
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input: Union[str, List, Dict, Message, BaseModel, List[Message]],
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*,
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stream: Optional[bool] = None,
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stream_intermediate_steps: Optional[bool] = None,
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user_id: Optional[str] = None,
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session_id: Optional[str] = None,
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session_state: Optional[Dict[str, Any]] = None,
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audio: Optional[Sequence[Any]] = None,
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images: Optional[Sequence[Any]] = None,
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videos: Optional[Sequence[Any]] = None,
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files: Optional[Sequence[Any]] = None,
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retries: Optional[int] = None,
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knowledge_filters: Optional[Dict[str, Any]] = None,
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add_history_to_context: Optional[bool] = None,
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add_dependencies_to_context: Optional[bool] = None,
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add_session_state_to_context: Optional[bool] = None,
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dependencies: Optional[Dict[str, Any]] = None,
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metadata: Optional[Dict[str, Any]] = None,
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yield_run_response: bool = False,
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debug_mode: Optional[bool] = None,
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**kwargs: Any,
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) -> Union[RunOutput, Iterator[Union[RunOutputEvent, RunOutput]]]:
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"""
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@staticmethod
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def analyze(query: str) -> str:
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# Mock analisi social
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return "💬 Sentiment social: forte interesse retail su nuove altcoin emergenti."
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@@ -94,3 +94,8 @@ class MarketAPIsTool(BaseWrapper, Toolkit):
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if not self._aggregation_enabled:
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raise RuntimeError("L'aggregazione deve essere abilitata per usare questo metodo")
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return self._get_aggregator().get_aggregated_product_with_debug(asset_id)
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# TODO definire istruzioni per gli agenti di mercato
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MARKET_INSTRUCTIONS = """
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"""
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@@ -1,11 +1,9 @@
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from typing import List
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from agno.team import Team
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from agno.utils.log import log_info
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from app.agents.market_agent import MarketAgent
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from app.agents.news_agent import NewsAgent
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from app.agents.social_agent import SocialAgent
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from app.news import NewsAPIsTool, NEWS_INSTRUCTIONS
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from app.social import SocialAPIsTool, SOCIAL_INSTRUCTIONS
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from app.markets import MarketAPIsTool, MARKET_INSTRUCTIONS
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from app.models import AppModels
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from app.predictor import PredictorStyle, PredictorInput, PredictorOutput, PREDICTOR_INSTRUCTIONS
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@@ -17,12 +15,38 @@ class Pipeline:
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def __init__(self):
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# Inizializza gli agenti
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self.market_agent = MarketAgent()
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self.news_agent = NewsAgent()
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self.social_agent = SocialAgent()
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market_agent = AppModels.OLLAMA_QWEN_1B.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 = AppModels.OLLAMA_QWEN_1B.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 = AppModels.OLLAMA_QWEN_1B.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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# Crea il Team
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self.team = Team(name="CryptoAnalysisTeam", members=[self.market_agent, self.news_agent, self.social_agent])
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prompt = """
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You are the coordinator of a team of analysts specialized in cryptocurrency market analysis.
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Your role is to gather insights from various sources, including market data, news articles, and social media trends.
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Based on the information provided by your team members, you will synthesize a comprehensive sentiment analysis for each cryptocurrency discussed.
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Your analysis should consider the following aspects:
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1. Market Trends: Evaluate the current market trends and price movements.
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2. News Impact: Assess the impact of recent news articles on market sentiment.
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3. Social Media Buzz: Analyze social media discussions and trends related to the cryptocurrencies.
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Your final output should be a well-rounded sentiment analysis that can guide investment decisions.
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""" # TODO migliorare il prompt
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self.team = Team(
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model = AppModels.OLLAMA_QWEN_1B.get_model(prompt),
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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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# Modelli disponibili e Predictor
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self.available_models = AppModels.availables()
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@@ -76,8 +100,8 @@ class Pipeline:
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return output
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def list_providers(self) -> List[str]:
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def list_providers(self) -> list[str]:
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return [m.name for m in self.available_models]
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def list_styles(self) -> List[str]:
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def list_styles(self) -> list[str]:
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return [s.value for s in self.styles]
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