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Update chat interface (#70)

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* Update chat interface to fill height and width in Gradio blocks
* Implement asynchronous streaming for Gradio responses and enhance pipeline event handling
* Refactor tool event handling to provide user-friendly messages and add utility function for descriptive tool actions
This commit was merged in pull request #70.
This commit is contained in:
trojanhorse47
2025-10-31 14:24:39 +01:00
committed by GitHub
parent fe6974e938
commit 5bda06823e
10 changed files with 165 additions and 67 deletions
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90
src/app/agents/pipeline.py
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@@ -1,8 +1,7 @@
import asyncio
from enum import Enum
import logging
import random
from typing import Any, Callable
from typing import Any, AsyncGenerator, Callable
from agno.agent import RunEvent
from agno.run.workflow import WorkflowRunEvent
from agno.workflow.types import StepInput, StepOutput
@@ -13,28 +12,34 @@ from app.agents.core import *
logging = logging.getLogger("pipeline")
class PipelineEvent(str, Enum):
QUERY_CHECK = "Query Check"
QUERY_ANALYZER = "Query Analyzer"
QUERY_CHECK_END = "Query Check End"
INFO_RECOVERY = "Info Recovery"
INFO_RECOVERY_END = "Info Recovery End"
REPORT_GENERATION = "Report Generation"
REPORT_TRANSLATION = "Report Translation"
RUN_FINISHED = WorkflowRunEvent.workflow_completed.value
TOOL_USED = RunEvent.tool_call_completed.value
REPORT_GENERATION_END = "Report Generation End"
TOOL_USED = RunEvent.tool_call_started.value
TOOL_USED_END = RunEvent.tool_call_completed.value
RUN_END = WorkflowRunEvent.workflow_completed.value
def check_event(self, event: str, step_name: str) -> bool:
return event == self.value or (WorkflowRunEvent.step_completed == event and step_name == self.value)
if event == self.value:
return True
index = self.value.rfind(" End")
value = self.value[:index] if index > -1 else self.value
step_state = WorkflowRunEvent.step_completed if index > -1 else WorkflowRunEvent.step_started
return step_name == value and step_state == event
@classmethod
def get_log_events(cls, run_id: int) -> list[tuple['PipelineEvent', Callable[[Any], None]]]:
def get_log_events(cls, run_id: int) -> list[tuple['PipelineEvent', Callable[[Any], str | None]]]:
return [
(PipelineEvent.QUERY_CHECK, lambda _: logging.info(f"[{run_id}] Query Check completed.")),
(PipelineEvent.QUERY_ANALYZER, lambda _: logging.info(f"[{run_id}] Query Analyzer completed.")),
(PipelineEvent.INFO_RECOVERY, lambda _: logging.info(f"[{run_id}] Info Recovery completed.")),
(PipelineEvent.REPORT_GENERATION, lambda _: logging.info(f"[{run_id}] Report Generation completed.")),
(PipelineEvent.TOOL_USED, lambda e: logging.info(f"[{run_id}] Tool used [{e.tool.tool_name} {e.tool.tool_args}] by {e.agent_name}.")),
(PipelineEvent.RUN_FINISHED, lambda _: logging.info(f"[{run_id}] Run completed.")),
(PipelineEvent.QUERY_CHECK_END, lambda _: logging.info(f"[{run_id}] Query Check completed.")),
(PipelineEvent.INFO_RECOVERY_END, lambda _: logging.info(f"[{run_id}] Info Recovery completed.")),
(PipelineEvent.REPORT_GENERATION_END, lambda _: logging.info(f"[{run_id}] Report Generation completed.")),
(PipelineEvent.TOOL_USED_END, lambda e: logging.info(f"[{run_id}] Tool used [{e.tool.tool_name} {e.tool.tool_args}] by {e.agent_name}.")),
(PipelineEvent.RUN_END, lambda _: logging.info(f"[{run_id}] Run completed.")),
]
@@ -53,7 +58,7 @@ class Pipeline:
"""
self.inputs = inputs
def interact(self, listeners: list[tuple[PipelineEvent, Callable[[Any], None]]] = []) -> str:
async def interact(self, listeners: list[tuple[PipelineEvent, Callable[[Any], str | None]]] = []) -> str:
"""
Esegue la pipeline di agenti per rispondere alla query dell'utente.
Args:
@@ -61,9 +66,12 @@ class Pipeline:
Returns:
La risposta generata dalla pipeline.
"""
return asyncio.run(self.interact_async(listeners))
response = ""
async for chunk in self.interact_stream(listeners):
response = chunk
return response
async def interact_async(self, listeners: list[tuple[PipelineEvent, Callable[[Any], None]]] = []) -> str:
async def interact_stream(self, listeners: list[tuple[PipelineEvent, Callable[[Any], str | None]]] = []) -> AsyncGenerator[str, None]:
"""
Versione asincrona che esegue la pipeline di agenti per rispondere alla query dell'utente.
Args:
@@ -81,9 +89,8 @@ class Pipeline:
)
workflow = self.build_workflow()
result = await self.run(workflow, query, events=events)
return result
async for item in self.run_stream(workflow, query, events=events):
yield item
def build_workflow(self) -> Workflow:
"""
@@ -99,7 +106,8 @@ class Pipeline:
# 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)
stop = (not val.is_crypto) if isinstance(val, QueryOutputs) else True
return StepOutput(stop=stop)
query_check = Step(name=PipelineEvent.QUERY_CHECK, agent=query_check)
info_recovery = Step(name=PipelineEvent.INFO_RECOVERY, team=team)
@@ -114,33 +122,39 @@ class Pipeline:
])
@classmethod
async def run(cls, workflow: Workflow, query: QueryInputs, events: list[tuple[PipelineEvent, Callable[[Any], None]]]) -> str:
async def run_stream(cls, workflow: Workflow, query: QueryInputs, events: list[tuple[PipelineEvent, Callable[[Any], str | None]]]) -> AsyncGenerator[str, None]:
"""
Esegue il workflow e gestisce gli eventi tramite le callback fornite.
Esegue il workflow e restituisce gli eventi di stato e il risultato finale.
Args:
workflow: istanza di Workflow da eseguire
query: query dell'utente da passare al workflow
events: dizionario di callback per eventi specifici (opzionale)
Returns:
La risposta generata dal workflow.
workflow: L'istanza di Workflow da eseguire
query: Gli input della query
events: La lista di eventi e callback da gestire durante l'esecuzione.
Yields:
Aggiornamenti di stato e la risposta finale generata dal workflow.
"""
iterator = await workflow.arun(query, stream=True, stream_intermediate_steps=True)
content = None
async for event in iterator:
step_name = getattr(event, 'step_name', '')
# Chiama i listeners (se presenti) per ogni evento
for app_event, listener in events:
if app_event.check_event(event.event, step_name):
listener(event)
if event.event == WorkflowRunEvent.step_completed:
update = listener(event)
if update: yield update
# Salva il contenuto finale quando uno step è completato
if event.event == WorkflowRunEvent.step_completed.value:
content = getattr(event, 'content', '')
# Restituisce la risposta finale
if content and isinstance(content, str):
think_str = "</think>"
think = content.rfind(think_str)
return content[(think + len(think_str)):] if think != -1 else content
if content and isinstance(content, QueryOutputs):
return content.response
logging.error(f"No output from workflow: {content}")
return "No output from workflow, something went wrong."
yield content[(think + len(think_str)):] if think != -1 else content
elif content and isinstance(content, QueryOutputs):
yield content.response
else:
logging.error(f"No output from workflow: {content}")
yield "Nessun output dal workflow, qualcosa è andato storto."