12 fix docs (#13)
* fix dependencies uv.lock * refactor test markers for clarity * refactor: clean up imports and remove unused files * refactor: remove unused agent files and clean up market API instructions * refactor: enhance wrapper initialization with keyword arguments and clean up tests * refactor: remove PublicBinanceAgent * refactor: aggregator - simplified MarketDataAggregator and related models to functions * refactor: update README and .env.example to reflect the latest changes to the project * refactor: simplify product info and price creation in YFinanceWrapper * refactor: remove get_all_products method from market API wrappers and update documentation * fix: environment variable assertions * refactor: remove status attribute from ProductInfo and update related methods to use timestamp_ms * feat: implement aggregate_history_prices function to calculate hourly price averages * refactor: update docker-compose and app.py for improved environment variable handling and compatibility * feat: add detailed market instructions and improve error handling in price aggregation methods * feat: add aggregated news retrieval methods for top headlines and latest news * refactor: improve error messages in WrapperHandler for better clarity * fix: correct quote currency extraction in create_product_info and remove debug prints from tests
This commit was merged in pull request #13.
This commit is contained in:
Giacomo Bertolazzi
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GitHub
GitHub
parent
a8755913d8
commit
d2fbc0ceea
@@ -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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