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Refactor agent handling in Pipeline; add tests for report generation and team agent responses

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This commit is contained in:
Giacomo Bertolazzi
2025-10-20 11:46:56 +02:00
parent 9513ab0932
commit 1664fb6d89
5 changed files with 120 additions and 39 deletions
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46
src/app/agents/core.py
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@@ -1,5 +1,10 @@
from pydantic import BaseModel
from agno.agent import Agent
from agno.team import Team
from agno.tools.reasoning import ReasoningTools
from app.api.tools import *
from app.configs import AppConfig
from app.agents.prompts import *
@@ -11,8 +16,6 @@ class QueryOutputs(BaseModel):
response: str
is_crypto: bool
class PipelineInputs:
"""
Classe necessaria per passare gli input alla Pipeline.
@@ -70,7 +73,46 @@ class PipelineInputs:
"""
return [strat.label for strat in self.configs.strategies]
def get_query_inputs(self) -> QueryInputs:
"""
Restituisce gli input per l'agente di verifica della query.
"""
return QueryInputs(
user_query=self.user_query,
strategy=self.strategy.label,
)
# ======================
# Agent getters
# ======================
def get_agent_team(self) -> Team:
market, news, social = self.get_tools()
market_agent = self.team_model.get_agent(MARKET_INSTRUCTIONS, "Market Agent", tools=[market])
news_agent = self.team_model.get_agent(NEWS_INSTRUCTIONS, "News Agent", tools=[news])
social_agent = self.team_model.get_agent(SOCIAL_INSTRUCTIONS, "Socials Agent", tools=[social])
return Team(
model=self.team_leader_model.get_model(TEAM_LEADER_INSTRUCTIONS),
name="CryptoAnalysisTeam",
tools=[ReasoningTools()],
members=[market_agent, news_agent, social_agent],
)
def get_agent_query_checker(self) -> Agent:
return self.query_analyzer_model.get_agent(QUERY_CHECK_INSTRUCTIONS, "Query Check Agent", output_schema=QueryOutputs)
def get_agent_report_generator(self) -> Agent:
return self.report_generation_model.get_agent(REPORT_GENERATION_INSTRUCTIONS, "Report Generator Agent")
def get_tools(self) -> tuple[MarketAPIsTool, NewsAPIsTool, SocialAPIsTool]:
"""
Restituisce la lista di tools disponibili per gli agenti.
"""
api = self.configs.api
market_tool = MarketAPIsTool(currency=api.currency)
market_tool.handler.set_retries(api.retry_attempts, api.retry_delay_seconds)
news_tool = NewsAPIsTool()
news_tool.handler.set_retries(api.retry_attempts, api.retry_delay_seconds)
social_tool = SocialAPIsTool()
social_tool.handler.set_retries(api.retry_attempts, api.retry_delay_seconds)
return market_tool, news_tool, social_tool

40
src/app/agents/pipeline.py
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@@ -4,14 +4,10 @@ import logging
import random
from typing import Any, Callable
from agno.agent import RunEvent
from agno.team import Team
from agno.tools.reasoning import ReasoningTools
from agno.run.workflow import WorkflowRunEvent
from agno.workflow.types import StepInput, StepOutput
from agno.workflow.step import Step
from agno.workflow.workflow import Workflow
from app.api.tools import *
from app.agents.prompts import *
from app.agents.core import *
logging = logging.getLogger("pipeline")
@@ -91,28 +87,18 @@ class Pipeline:
L'istanza di Workflow costruita.
"""
# Step 1: Crea gli agenti e il team
q_check_agent = self.inputs.query_analyzer_model.get_agent(instructions=QUERY_CHECK_INSTRUCTIONS, name="QueryCheckAgent", output_schema=QueryOutputs)
report_agent = self.inputs.report_generation_model.get_agent(instructions=REPORT_GENERATION_INSTRUCTIONS, name="ReportGeneratorAgent")
market_tool, news_tool, social_tool = self.get_tools()
market_agent = self.inputs.team_model.get_agent(instructions=MARKET_INSTRUCTIONS, name="MarketAgent", tools=[market_tool])
news_agent = self.inputs.team_model.get_agent(instructions=NEWS_INSTRUCTIONS, name="NewsAgent", tools=[news_tool])
social_agent = self.inputs.team_model.get_agent(instructions=SOCIAL_INSTRUCTIONS, name="SocialAgent", tools=[social_tool])
team = Team(
model=self.inputs.team_leader_model.get_model(COORDINATOR_INSTRUCTIONS),
name="CryptoAnalysisTeam",
tools=[ReasoningTools()],
members=[market_agent, news_agent, social_agent],
)
team = self.inputs.get_agent_team()
query_check = self.inputs.get_agent_query_checker()
report = self.inputs.get_agent_report_generator()
# Step 2: Crea gli steps
def condition_query_ok(step_input: StepInput) -> StepOutput:
val = step_input.previous_step_content
return StepOutput(stop=not val.is_crypto) if isinstance(val, QueryOutputs) else StepOutput(stop=True)
query_check = Step(name=PipelineEvent.QUERY_CHECK, agent=q_check_agent)
query_check = Step(name=PipelineEvent.QUERY_CHECK, agent=query_check)
info_recovery = Step(name=PipelineEvent.INFO_RECOVERY, team=team)
report_generation = Step(name=PipelineEvent.REPORT_GENERATION, agent=report_agent)
report_generation = Step(name=PipelineEvent.REPORT_GENERATION, agent=report)
# Step 3: Ritorna il workflow completo
return Workflow(name="App Workflow", steps=[
@@ -122,22 +108,6 @@ class Pipeline:
report_generation
])
def get_tools(self) -> tuple[MarketAPIsTool, NewsAPIsTool, SocialAPIsTool]:
"""
Restituisce la lista di tools disponibili per gli agenti.
"""
api = self.inputs.configs.api
market_tool = MarketAPIsTool(currency=api.currency)
market_tool.handler.set_retries(api.retry_attempts, api.retry_delay_seconds)
news_tool = NewsAPIsTool()
news_tool.handler.set_retries(api.retry_attempts, api.retry_delay_seconds)
social_tool = SocialAPIsTool()
social_tool.handler.set_retries(api.retry_attempts, api.retry_delay_seconds)
return (market_tool, news_tool, social_tool)
@classmethod
async def run(cls, workflow: Workflow, query: QueryInputs, events: list[tuple[PipelineEvent, Callable[[Any], None]]]) -> str:
"""

4
src/app/agents/prompts/__init__.py
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@@ -6,7 +6,7 @@ def __load_prompt(file_name: str) -> str:
file_path = __PROMPTS_PATH / file_name
return file_path.read_text(encoding='utf-8').strip()
COORDINATOR_INSTRUCTIONS = __load_prompt("team_leader.txt")
TEAM_LEADER_INSTRUCTIONS = __load_prompt("team_leader.txt")
MARKET_INSTRUCTIONS = __load_prompt("team_market.txt")
NEWS_INSTRUCTIONS = __load_prompt("team_news.txt")
SOCIAL_INSTRUCTIONS = __load_prompt("team_social.txt")
@@ -14,7 +14,7 @@ QUERY_CHECK_INSTRUCTIONS = __load_prompt("query_check.txt")
REPORT_GENERATION_INSTRUCTIONS = __load_prompt("report_generation.txt")
__all__ = [
"COORDINATOR_INSTRUCTIONS",
"TEAM_LEADER_INSTRUCTIONS",
"MARKET_INSTRUCTIONS",
"NEWS_INSTRUCTIONS",
"SOCIAL_INSTRUCTIONS",

31
tests/agents/test_report.py Normal file
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@@ -0,0 +1,31 @@
import pytest
from app.agents.prompts import REPORT_GENERATION_INSTRUCTIONS
from app.configs import AppConfig
class TestReportGenerationAgent:
@pytest.fixture(autouse=True)
def setup(self):
self.configs = AppConfig.load()
self.model = self.configs.get_model_by_name("qwen3:1.7b")
self.agent = self.model.get_agent(REPORT_GENERATION_INSTRUCTIONS)
def test_report_generation(self):
sample_data = """
The analysis reported from the Market Agent have highlighted the following key metrics for the cryptocurrency market:
Bitcoin (BTC) has shown strong performance over the last 24 hours with a price of $30,000 and a Market Cap of $600 Billion
Ethereum (ETH) is currently priced at $2,000 with a Market Cap of $250 Billion and a 24h Volume of $20 Billion.
The overall market sentiment is bullish with a 5% increase in total market capitalization.
No significant regulatory news has been reported and the social media sentiment remains unknown.
"""
response = self.agent.run(sample_data) #type: ignore
assert response is not None
assert response.content is not None
content = response.content
assert isinstance(content, str)
print(content)
assert "Bitcoin" in content
assert "Ethereum" in content
assert "Summary" in content

38
tests/agents/test_team.py Normal file
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@@ -0,0 +1,38 @@
import asyncio
import pytest
from app.agents.core import PipelineInputs
from app.agents.prompts import *
from app.configs import AppConfig
# fix warning about no event loop
@pytest.fixture(scope="session", autouse=True)
def event_loop():
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
yield loop
loop.close()
@pytest.mark.slow
class TestTeamAgent:
@pytest.fixture(autouse=True)
def setup(self):
self.configs = AppConfig.load()
self.configs.agents.team_model = "qwen3:1.7b"
self.configs.agents.team_leader_model = "qwen3:1.7b"
self.inputs = PipelineInputs(self.configs)
self.team = self.inputs.get_agent_team()
def test_team_agent_response(self):
self.inputs.user_query = "Is Bitcoin a good investment now?"
inputs = self.inputs.get_query_inputs()
response = self.team.run(inputs) # type: ignore
assert response is not None
assert response.content is not None
content = response.content
print(content)
assert isinstance(content, str)
assert "Bitcoin" in content
assert False