bugfix Alex 2026-06-30

This commit is contained in:
2026-07-01 07:15:38 -03:00
parent d603a01039
commit 9607c723a0
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###############################################################################
# AI AGENT PLATFORM - CONFIGURAÇÃO ÚNICA
# Este arquivo é lido por Pydantic Settings no framework e no backend template.
###############################################################################
APP_NAME=ai-agent-template
APP_ENV=local
LOG_LEVEL=INFO
API_HOST=0.0.0.0
API_PORT=8000
CORS_ORIGINS=http://localhost:5173,http://127.0.0.1:5173
###############################################################################
# LLM - OCI Generative AI como provider principal
###############################################################################
# Opções: mock, oci_openai, oci_sdk, openai_compatible
LLM_PROVIDER=oci_openai
LLM_TEMPERATURE=0.2
LLM_MAX_TOKENS=2048
LLM_TIMEOUT_SECONDS=120
# OCI OpenAI-compatible endpoint
OCI_GENAI_BASE_URL=https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/openai/v1
OCI_GENAI_MODEL=openai.gpt-4.1
OCI_GENAI_API_KEY=sk-ph3FgX6iP3fxAQCXb9IpPIDTadkeeYAWntUWhzcWysIM6zsS
OCI_GENAI_PROJECT_OCID=
# OCI SDK / signer / profiles
OCI_CONFIG_FILE=~/.oci/config
OCI_PROFILE=LATINOAMERICA-Chicago
OCI_COMPARTMENT_ID=ocid1.compartment.oc1..aaaaaaaaexpiw4a7dio64mkfv2t273s2hgdl6mgfvvyv7tycalnjlvpvfl3q
OCI_REGION=us-chicago-1
###############################################################################
# Persistência
###############################################################################
# Opções: memory, autonomous, mongodb
SESSION_REPOSITORY_PROVIDER=sqlite
MEMORY_REPOSITORY_PROVIDER=sqlite
CHECKPOINT_REPOSITORY_PROVIDER=sqlite
SQLITE_DB_PATH=./data/agent_framework.db
# Autonomous Database
ADB_USER=admin
ADB_PASSWORD=Moniquinha1972
ADB_DSN=oradb23ai_high
ADB_WALLET_LOCATION=/mnt/d/Dropbox/ORACLE/LatinoAmerica/Wallet_ORADB23ai
ADB_WALLET_PASSWORD=Moniquinha1972
ADB_TABLE_PREFIX=AGENTFW
# MongoDB - também pode representar Autonomous usando API compatível com Mongo, se habilitada no ambiente
MONGODB_URI=mongodb://mongo:mongopassword@localhost:27017
MONGODB_DATABASE=agent_platform
# Redis
REDIS_URL=redis://localhost:6379/0
ENABLE_REDIS_CACHE=false
###############################################################################
# RAG / Vector / Graph
###############################################################################
VECTOR_STORE_PROVIDER=memory
GRAPH_STORE_PROVIDER=memory
RAG_TOP_K=5
EMBEDDING_PROVIDER=mock
OCI_EMBEDDING_MODEL=cohere.embed-multilingual-v3.0
RAG_FILE_GLOBS=*.md,*.txt,*.yaml,*.yml,*.json
###############################################################################
# Observabilidade
###############################################################################
ENABLE_LANGFUSE=true
LANGFUSE_TRACE_MODE=compact # Opcional: verbose, compact
LANGFUSE_PUBLIC_KEY=pk-lf-2f9da109-5b0f-4c78-b61d-9598ed787eba
LANGFUSE_SECRET_KEY=sk-lf-a4cb0cdd-f2ea-4468-9911-cebeb91ba944
LANGFUSE_HOST=http://localhost:3005
ENABLE_OTEL=false
OTEL_EXPORTER_OTLP_ENDPOINT=
OTEL_SERVICE_NAME=ai-agent-template
###############################################################################
# Analytics / Observer corporativo
###############################################################################
# Quando true, AgentObserver publica eventos IC.*, NOC.* e GRL.* nos providers abaixo.
ENABLE_ANALYTICS=false
# Providers aceitos: oci_streaming,pubsub,noop
ANALYTICS_PROVIDERS=pubsub
# Compatibilidade FIRST/TIM: pode informar AGENT_PUBSUB_TOPIC diretamente.
AGENT_PUBSUB_TOPIC=
GCP_PUBSUB_TOPIC_PATH=
GCP_PROJECT_ID=
GCP_PUBSUB_TOPIC=
GCP_PUBSUB_TIMEOUT_SECONDS=30
# Credencial GCP segue padrão Google:
# GOOGLE_APPLICATION_CREDENTIALS=/secrets/gcp-service-account.json
###############################################################################
# OCI Streaming
###############################################################################
ENABLE_OCI_STREAMING=false
OCI_STREAM_ENDPOINT=
OCI_STREAM_OCID=
OCI_STREAM_PARTITION_KEY=agent-events
###############################################################################
# Guardrails, Judges, Supervisor
###############################################################################
ENABLE_INPUT_GUARDRAILS=true
ENABLE_OUTPUT_GUARDRAILS=true
ENABLE_JUDGES=true
ENABLE_SUPERVISOR=true
ENABLE_OUTPUT_SUPERVISOR=true
ENABLE_PARALLEL_GUARDRAILS=true
GUARDRAILS_FAIL_FAST=true
OUTPUT_SUPERVISOR_MAX_RETRIES=3
GUARDRAILS_CONFIG_PATH=./config/guardrails.yaml
JUDGES_CONFIG_PATH=./config/judges.yaml
PROMPT_POLICY_PATH=./config/prompt_policy.yaml
###############################################################################
# Gateway de canais
###############################################################################
DEFAULT_CHANNEL=web
ENABLE_VOICE_ADAPTER=true
ENABLE_WHATSAPP_ADAPTER=true
ENABLE_TEXT_ADAPTER=true
#################################################
# ENTERPRISE ROUTING
#################################################
# Arquivo YAML com intents, keywords, políticas de estado e fallback.
ROUTING_CONFIG_PATH=./config/routing.yaml
# true = usa LLM para classificar quando keywords/estado não resolverem.
# Em produção, costuma ser útil; em desenvolvimento, false evita custo e latência.
ENABLE_LLM_ROUTER=true
###############################################################################
# MCP / Tools
###############################################################################
ENABLE_MCP_TOOLS=true
MCP_SERVERS_CONFIG_PATH=./agent_template_backend/config/mcp_servers.yaml
TOOLS_CONFIG_PATH=./agent_template_backend/config/tools.yaml
MCP_TOOL_TIMEOUT_SECONDS=30
# router = EnterpriseRouter seleciona um agente; supervisor = pode acionar múltiplos agentes
ROUTING_MODE=router
# Usage/cost accounting
USAGE_REPOSITORY_PROVIDER=sqlite
IDENTITY_CONFIG_PATH=./agent_template_backend/config/identity.yaml
MCP_PARAMETER_MAPPING_PATH=./agent_template_backend/config/mcp_parameter_mapping.yaml
# -----------------------------------------------------------------------------
# ConversationSummaryMemory / compressão de contexto conversacional
# -----------------------------------------------------------------------------
ENABLE_CONVERSATION_SUMMARY_MEMORY=true
MEMORY_CONTEXT_STRATEGY=summary
MEMORY_HISTORY_LIMIT=80
MEMORY_RECENT_MESSAGES_LIMIT=8
MEMORY_SUMMARY_TRIGGER_MESSAGES=20
MEMORY_MAX_SUMMARY_CHARS=6000
MEMORY_SUMMARY_USE_LLM=true
MEMORY_INJECT_RECENT_MESSAGES=true
MEMORY_INJECT_SUMMARY=true

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from __future__ import annotations
import pytest
from agent_framework.observability.context import clear_observability_context
from agent_framework.observability.telemetry import Telemetry
class Settings:
ENABLE_LANGFUSE = False
ENABLE_OTEL = False
LANGFUSE_TRACE_MODE = "compact"
class FakeObservation:
_next_id = 1
def __init__(self, kwargs):
self.kwargs = kwargs
self.id = f"obs-{FakeObservation._next_id}"
self.trace_id = "trace-123"
FakeObservation._next_id += 1
self.updates = []
self.trace_updates = []
self.trace_io_updates = []
def update(self, **kwargs):
self.updates.append(kwargs)
def update_trace(self, **kwargs):
self.trace_updates.append(kwargs)
def set_trace_io(self, **kwargs):
self.trace_io_updates.append(kwargs)
class FakeContextManager:
def __init__(self, observation):
self.observation = observation
def __enter__(self):
return self.observation
def __exit__(self, exc_type, exc, tb):
return False
class FakePropagationContext:
def __init__(self, owner, kwargs):
self.owner = owner
self.kwargs = kwargs
def __enter__(self):
self.owner.propagations.append(self.kwargs)
def __exit__(self, exc_type, exc, tb):
return False
class FakeLangfuse:
def __init__(self, *, legacy_api: bool = False):
self.observations = []
self.propagations = []
self.flush_count = 0
self.api = FakeApi() if legacy_api else None
def start_as_current_observation(self, **kwargs):
observation = FakeObservation(kwargs)
self.observations.append(observation)
return FakeContextManager(observation)
def propagate_attributes(self, **kwargs):
return FakePropagationContext(self, kwargs)
def flush(self):
self.flush_count += 1
class FakeIngestionResponse:
errors = []
successes = []
class FakeIngestion:
def __init__(self):
self.batches = []
def batch(self, *, batch, metadata=None):
self.batches.append({"batch": batch, "metadata": metadata})
return FakeIngestionResponse()
class FakeApi:
def __init__(self):
self.ingestion = FakeIngestion()
def telemetry_with_fake_langfuse(*, legacy_api: bool = False):
FakeObservation._next_id = 1
telemetry = Telemetry(Settings())
telemetry.enabled = True
telemetry.langfuse = FakeLangfuse(legacy_api=legacy_api)
return telemetry
@pytest.mark.asyncio
async def test_compact_keeps_root_output_and_shows_only_aga_noc_events():
clear_observability_context()
telemetry = telemetry_with_fake_langfuse()
async with telemetry.span("agent.gateway_message", session_id="s1", input={"request": "cms"}, _root_span=True) as span:
await telemetry.event("IC.INTERNAL", {"step": "hidden"}, kind="ic")
await telemetry.event("NOC.001", {"step": "visible"}, kind="noc")
await telemetry.event("AGA.010", {"step": "visible"}, kind="ic")
span.set_output({"answer": "ok"})
names = [obs.kwargs["name"] for obs in telemetry.langfuse.observations]
assert names == ["agent.gateway_message", "NOC.001", "AGA.010"]
root = telemetry.langfuse.observations[0]
assert root.updates[-1]["input"] == {"request": "cms"}
assert root.updates[-1]["output"] == {"answer": "ok"}
assert root.trace_io_updates[-1] == {"input": {"request": "cms"}, "output": {"answer": "ok"}}
assert telemetry.langfuse.observations[1].kwargs.get("trace_context") is None
assert telemetry.langfuse.observations[2].kwargs.get("trace_context") is None
assert telemetry.langfuse.propagations[-1]["trace_name"] == "agent.gateway_message"
aggregated = root.updates[-1]["metadata"]["aggregated_events"]
assert [event["name"] for event in aggregated] == ["IC.INTERNAL", "NOC.001", "AGA.010"]
@pytest.mark.asyncio
async def test_compact_generation_records_io_model_parameters_and_usage_details():
clear_observability_context()
telemetry = telemetry_with_fake_langfuse()
async with telemetry.span("agent.gateway_message", session_id="s1", input={"request": "cms"}, _root_span=True):
async with telemetry.generation_span(
name="llm.test",
model="test-model",
input=[{"role": "user", "content": "ping"}],
metadata={"profile_name": "test"},
model_parameters={"temperature": 0.2, "max_tokens": 100},
) as generation:
generation.set_output("pong")
generation.set_usage({"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2, "cost_usd": 0.01})
generation = telemetry.langfuse.observations[1]
assert generation.kwargs["name"] == "llm.test"
assert generation.kwargs["as_type"] == "generation"
assert generation.kwargs["input"] == [{"role": "user", "content": "ping"}]
assert generation.kwargs["model"] == "test-model"
assert generation.kwargs["model_parameters"] == {"temperature": 0.2, "max_tokens": 100}
assert "usage" not in generation.kwargs
assert "usage_details" not in generation.kwargs
assert generation.updates[-1]["input"] == [{"role": "user", "content": "ping"}]
assert generation.updates[-1]["output"] == "pong"
assert generation.updates[-1]["usage_details"] == {"input": 1, "output": 1}
assert generation.updates[-1]["cost_details"] == {"total": 0.01}
assert generation.kwargs.get("trace_context") is None
@pytest.mark.asyncio
async def test_legacy_io_fallback_updates_same_root_and_generation_observations():
clear_observability_context()
telemetry = telemetry_with_fake_langfuse(legacy_api=True)
async with telemetry.span("agent.gateway_message", session_id="s1", input={"request": "cms"}, _root_span=True) as root:
async with telemetry.generation_span(
name="llm.test",
model="test-model",
input=[{"role": "user", "content": "ping"}],
model_parameters={"temperature": 0.2},
) as generation:
generation.set_output("pong")
generation.set_usage({"prompt_tokens": 2, "completion_tokens": 3, "total_tokens": 5})
root.set_output({"answer": "ok"})
batches = telemetry.langfuse.api.ingestion.batches
assert [event.type for item in batches for event in item["batch"]] == ["generation-update", "span-update"]
generation_event = batches[0]["batch"][0]
assert generation_event.body.id == "obs-2"
assert generation_event.body.trace_id == "trace-123"
assert generation_event.body.input == [{"role": "user", "content": "ping"}]
assert generation_event.body.output == "pong"
assert generation_event.body.usage_details == {"input": 2, "output": 3}
root_event = batches[1]["batch"][0]
assert root_event.body.id == "obs-1"
assert root_event.body.trace_id == "trace-123"
assert root_event.body.input == {"request": "cms"}
assert root_event.body.output == {"answer": "ok"}
assert len(telemetry.langfuse.observations) == 2