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https://github.com/hoshikawa2/agent_platform_oci.git
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bugfix Alex 2026-06-30
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164
.env
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164
.env
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###############################################################################
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# AI AGENT PLATFORM - CONFIGURAÇÃO ÚNICA
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# Este arquivo é lido por Pydantic Settings no framework e no backend template.
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###############################################################################
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APP_NAME=ai-agent-template
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APP_ENV=local
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LOG_LEVEL=INFO
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API_HOST=0.0.0.0
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API_PORT=8000
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CORS_ORIGINS=http://localhost:5173,http://127.0.0.1:5173
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###############################################################################
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# LLM - OCI Generative AI como provider principal
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###############################################################################
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# Opções: mock, oci_openai, oci_sdk, openai_compatible
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LLM_PROVIDER=oci_openai
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LLM_TEMPERATURE=0.2
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LLM_MAX_TOKENS=2048
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LLM_TIMEOUT_SECONDS=120
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# OCI OpenAI-compatible endpoint
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OCI_GENAI_BASE_URL=https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/openai/v1
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OCI_GENAI_MODEL=openai.gpt-4.1
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OCI_GENAI_API_KEY=sk-ph3FgX6iP3fxAQCXb9IpPIDTadkeeYAWntUWhzcWysIM6zsS
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OCI_GENAI_PROJECT_OCID=
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# OCI SDK / signer / profiles
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OCI_CONFIG_FILE=~/.oci/config
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OCI_PROFILE=LATINOAMERICA-Chicago
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OCI_COMPARTMENT_ID=ocid1.compartment.oc1..aaaaaaaaexpiw4a7dio64mkfv2t273s2hgdl6mgfvvyv7tycalnjlvpvfl3q
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OCI_REGION=us-chicago-1
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###############################################################################
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# Persistência
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###############################################################################
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# Opções: memory, autonomous, mongodb
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SESSION_REPOSITORY_PROVIDER=sqlite
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MEMORY_REPOSITORY_PROVIDER=sqlite
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CHECKPOINT_REPOSITORY_PROVIDER=sqlite
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SQLITE_DB_PATH=./data/agent_framework.db
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# Autonomous Database
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ADB_USER=admin
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ADB_PASSWORD=Moniquinha1972
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ADB_DSN=oradb23ai_high
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ADB_WALLET_LOCATION=/mnt/d/Dropbox/ORACLE/LatinoAmerica/Wallet_ORADB23ai
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ADB_WALLET_PASSWORD=Moniquinha1972
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ADB_TABLE_PREFIX=AGENTFW
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# MongoDB - também pode representar Autonomous usando API compatível com Mongo, se habilitada no ambiente
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MONGODB_URI=mongodb://mongo:mongopassword@localhost:27017
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MONGODB_DATABASE=agent_platform
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# Redis
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REDIS_URL=redis://localhost:6379/0
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ENABLE_REDIS_CACHE=false
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###############################################################################
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# RAG / Vector / Graph
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###############################################################################
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VECTOR_STORE_PROVIDER=memory
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GRAPH_STORE_PROVIDER=memory
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RAG_TOP_K=5
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EMBEDDING_PROVIDER=mock
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OCI_EMBEDDING_MODEL=cohere.embed-multilingual-v3.0
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RAG_FILE_GLOBS=*.md,*.txt,*.yaml,*.yml,*.json
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###############################################################################
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# Observabilidade
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###############################################################################
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ENABLE_LANGFUSE=true
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LANGFUSE_TRACE_MODE=compact # Opcional: verbose, compact
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LANGFUSE_PUBLIC_KEY=pk-lf-2f9da109-5b0f-4c78-b61d-9598ed787eba
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LANGFUSE_SECRET_KEY=sk-lf-a4cb0cdd-f2ea-4468-9911-cebeb91ba944
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LANGFUSE_HOST=http://localhost:3005
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ENABLE_OTEL=false
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OTEL_EXPORTER_OTLP_ENDPOINT=
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OTEL_SERVICE_NAME=ai-agent-template
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###############################################################################
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# Analytics / Observer corporativo
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###############################################################################
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# Quando true, AgentObserver publica eventos IC.*, NOC.* e GRL.* nos providers abaixo.
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ENABLE_ANALYTICS=false
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# Providers aceitos: oci_streaming,pubsub,noop
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ANALYTICS_PROVIDERS=pubsub
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# Compatibilidade FIRST/TIM: pode informar AGENT_PUBSUB_TOPIC diretamente.
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AGENT_PUBSUB_TOPIC=
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GCP_PUBSUB_TOPIC_PATH=
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GCP_PROJECT_ID=
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GCP_PUBSUB_TOPIC=
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GCP_PUBSUB_TIMEOUT_SECONDS=30
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# Credencial GCP segue padrão Google:
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# GOOGLE_APPLICATION_CREDENTIALS=/secrets/gcp-service-account.json
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###############################################################################
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# OCI Streaming
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###############################################################################
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ENABLE_OCI_STREAMING=false
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OCI_STREAM_ENDPOINT=
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OCI_STREAM_OCID=
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OCI_STREAM_PARTITION_KEY=agent-events
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###############################################################################
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# Guardrails, Judges, Supervisor
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###############################################################################
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ENABLE_INPUT_GUARDRAILS=true
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ENABLE_OUTPUT_GUARDRAILS=true
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ENABLE_JUDGES=true
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ENABLE_SUPERVISOR=true
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ENABLE_OUTPUT_SUPERVISOR=true
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ENABLE_PARALLEL_GUARDRAILS=true
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GUARDRAILS_FAIL_FAST=true
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OUTPUT_SUPERVISOR_MAX_RETRIES=3
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GUARDRAILS_CONFIG_PATH=./config/guardrails.yaml
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JUDGES_CONFIG_PATH=./config/judges.yaml
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PROMPT_POLICY_PATH=./config/prompt_policy.yaml
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###############################################################################
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# Gateway de canais
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###############################################################################
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DEFAULT_CHANNEL=web
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ENABLE_VOICE_ADAPTER=true
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ENABLE_WHATSAPP_ADAPTER=true
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ENABLE_TEXT_ADAPTER=true
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#################################################
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# ENTERPRISE ROUTING
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#################################################
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# Arquivo YAML com intents, keywords, políticas de estado e fallback.
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ROUTING_CONFIG_PATH=./config/routing.yaml
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# true = usa LLM para classificar quando keywords/estado não resolverem.
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# Em produção, costuma ser útil; em desenvolvimento, false evita custo e latência.
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ENABLE_LLM_ROUTER=true
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###############################################################################
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# MCP / Tools
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###############################################################################
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ENABLE_MCP_TOOLS=true
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MCP_SERVERS_CONFIG_PATH=./agent_template_backend/config/mcp_servers.yaml
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TOOLS_CONFIG_PATH=./agent_template_backend/config/tools.yaml
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MCP_TOOL_TIMEOUT_SECONDS=30
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# router = EnterpriseRouter seleciona um agente; supervisor = pode acionar múltiplos agentes
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ROUTING_MODE=router
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# Usage/cost accounting
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USAGE_REPOSITORY_PROVIDER=sqlite
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IDENTITY_CONFIG_PATH=./agent_template_backend/config/identity.yaml
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MCP_PARAMETER_MAPPING_PATH=./agent_template_backend/config/mcp_parameter_mapping.yaml
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# -----------------------------------------------------------------------------
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# ConversationSummaryMemory / compressão de contexto conversacional
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# -----------------------------------------------------------------------------
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ENABLE_CONVERSATION_SUMMARY_MEMORY=true
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MEMORY_CONTEXT_STRATEGY=summary
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MEMORY_HISTORY_LIMIT=80
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MEMORY_RECENT_MESSAGES_LIMIT=8
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MEMORY_SUMMARY_TRIGGER_MESSAGES=20
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MEMORY_MAX_SUMMARY_CHARS=6000
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MEMORY_SUMMARY_USE_LLM=true
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MEMORY_INJECT_RECENT_MESSAGES=true
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MEMORY_INJECT_SUMMARY=true
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194
tests/unit/test_telemetry_langfuse_compact.py
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194
tests/unit/test_telemetry_langfuse_compact.py
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from __future__ import annotations
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import pytest
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from agent_framework.observability.context import clear_observability_context
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from agent_framework.observability.telemetry import Telemetry
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class Settings:
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ENABLE_LANGFUSE = False
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ENABLE_OTEL = False
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LANGFUSE_TRACE_MODE = "compact"
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class FakeObservation:
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_next_id = 1
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def __init__(self, kwargs):
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self.kwargs = kwargs
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self.id = f"obs-{FakeObservation._next_id}"
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self.trace_id = "trace-123"
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FakeObservation._next_id += 1
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self.updates = []
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self.trace_updates = []
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self.trace_io_updates = []
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def update(self, **kwargs):
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self.updates.append(kwargs)
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def update_trace(self, **kwargs):
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self.trace_updates.append(kwargs)
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def set_trace_io(self, **kwargs):
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self.trace_io_updates.append(kwargs)
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class FakeContextManager:
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def __init__(self, observation):
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self.observation = observation
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def __enter__(self):
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return self.observation
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def __exit__(self, exc_type, exc, tb):
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return False
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class FakePropagationContext:
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def __init__(self, owner, kwargs):
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self.owner = owner
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self.kwargs = kwargs
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def __enter__(self):
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self.owner.propagations.append(self.kwargs)
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def __exit__(self, exc_type, exc, tb):
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return False
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class FakeLangfuse:
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def __init__(self, *, legacy_api: bool = False):
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self.observations = []
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self.propagations = []
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self.flush_count = 0
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self.api = FakeApi() if legacy_api else None
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def start_as_current_observation(self, **kwargs):
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observation = FakeObservation(kwargs)
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self.observations.append(observation)
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return FakeContextManager(observation)
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def propagate_attributes(self, **kwargs):
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return FakePropagationContext(self, kwargs)
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def flush(self):
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self.flush_count += 1
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class FakeIngestionResponse:
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errors = []
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successes = []
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class FakeIngestion:
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def __init__(self):
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self.batches = []
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def batch(self, *, batch, metadata=None):
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self.batches.append({"batch": batch, "metadata": metadata})
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return FakeIngestionResponse()
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class FakeApi:
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def __init__(self):
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self.ingestion = FakeIngestion()
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def telemetry_with_fake_langfuse(*, legacy_api: bool = False):
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FakeObservation._next_id = 1
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telemetry = Telemetry(Settings())
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telemetry.enabled = True
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telemetry.langfuse = FakeLangfuse(legacy_api=legacy_api)
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return telemetry
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@pytest.mark.asyncio
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async def test_compact_keeps_root_output_and_shows_only_aga_noc_events():
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clear_observability_context()
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telemetry = telemetry_with_fake_langfuse()
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async with telemetry.span("agent.gateway_message", session_id="s1", input={"request": "cms"}, _root_span=True) as span:
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await telemetry.event("IC.INTERNAL", {"step": "hidden"}, kind="ic")
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await telemetry.event("NOC.001", {"step": "visible"}, kind="noc")
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await telemetry.event("AGA.010", {"step": "visible"}, kind="ic")
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span.set_output({"answer": "ok"})
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names = [obs.kwargs["name"] for obs in telemetry.langfuse.observations]
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assert names == ["agent.gateway_message", "NOC.001", "AGA.010"]
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root = telemetry.langfuse.observations[0]
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assert root.updates[-1]["input"] == {"request": "cms"}
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assert root.updates[-1]["output"] == {"answer": "ok"}
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assert root.trace_io_updates[-1] == {"input": {"request": "cms"}, "output": {"answer": "ok"}}
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assert telemetry.langfuse.observations[1].kwargs.get("trace_context") is None
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assert telemetry.langfuse.observations[2].kwargs.get("trace_context") is None
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assert telemetry.langfuse.propagations[-1]["trace_name"] == "agent.gateway_message"
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aggregated = root.updates[-1]["metadata"]["aggregated_events"]
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assert [event["name"] for event in aggregated] == ["IC.INTERNAL", "NOC.001", "AGA.010"]
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@pytest.mark.asyncio
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async def test_compact_generation_records_io_model_parameters_and_usage_details():
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clear_observability_context()
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telemetry = telemetry_with_fake_langfuse()
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async with telemetry.span("agent.gateway_message", session_id="s1", input={"request": "cms"}, _root_span=True):
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async with telemetry.generation_span(
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name="llm.test",
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model="test-model",
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input=[{"role": "user", "content": "ping"}],
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metadata={"profile_name": "test"},
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model_parameters={"temperature": 0.2, "max_tokens": 100},
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) as generation:
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generation.set_output("pong")
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generation.set_usage({"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2, "cost_usd": 0.01})
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generation = telemetry.langfuse.observations[1]
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assert generation.kwargs["name"] == "llm.test"
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assert generation.kwargs["as_type"] == "generation"
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assert generation.kwargs["input"] == [{"role": "user", "content": "ping"}]
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assert generation.kwargs["model"] == "test-model"
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assert generation.kwargs["model_parameters"] == {"temperature": 0.2, "max_tokens": 100}
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assert "usage" not in generation.kwargs
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assert "usage_details" not in generation.kwargs
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assert generation.updates[-1]["input"] == [{"role": "user", "content": "ping"}]
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assert generation.updates[-1]["output"] == "pong"
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assert generation.updates[-1]["usage_details"] == {"input": 1, "output": 1}
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assert generation.updates[-1]["cost_details"] == {"total": 0.01}
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assert generation.kwargs.get("trace_context") is None
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@pytest.mark.asyncio
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async def test_legacy_io_fallback_updates_same_root_and_generation_observations():
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clear_observability_context()
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telemetry = telemetry_with_fake_langfuse(legacy_api=True)
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async with telemetry.span("agent.gateway_message", session_id="s1", input={"request": "cms"}, _root_span=True) as root:
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async with telemetry.generation_span(
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name="llm.test",
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model="test-model",
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input=[{"role": "user", "content": "ping"}],
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model_parameters={"temperature": 0.2},
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) as generation:
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generation.set_output("pong")
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generation.set_usage({"prompt_tokens": 2, "completion_tokens": 3, "total_tokens": 5})
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root.set_output({"answer": "ok"})
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batches = telemetry.langfuse.api.ingestion.batches
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assert [event.type for item in batches for event in item["batch"]] == ["generation-update", "span-update"]
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generation_event = batches[0]["batch"][0]
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assert generation_event.body.id == "obs-2"
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assert generation_event.body.trace_id == "trace-123"
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assert generation_event.body.input == [{"role": "user", "content": "ping"}]
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assert generation_event.body.output == "pong"
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assert generation_event.body.usage_details == {"input": 2, "output": 3}
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root_event = batches[1]["batch"][0]
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assert root_event.body.id == "obs-1"
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assert root_event.body.trace_id == "trace-123"
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assert root_event.body.input == {"request": "cms"}
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assert root_event.body.output == {"answer": "ok"}
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assert len(telemetry.langfuse.observations) == 2
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