chore: import upstream snapshot with attribution
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# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
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description: OpenTelemetry tracing with Python (protobuf format)
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providers:
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- python:provider.py
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prompts:
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- 'Explain how {{topic}} works in simple terms'
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tests:
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- vars:
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topic: 'quantum computing'
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metadata:
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tracingEnabled: true
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testCaseId: 'python-test-1'
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assert:
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# Ensure the main workflow span exists
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- type: trace-span-count
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value:
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pattern: 'rag_agent_workflow'
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min: 1
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max: 1
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# Ensure we retrieve exactly 3 documents
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- type: trace-span-count
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value:
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pattern: 'retrieve_document_*'
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min: 3
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max: 3
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# Ensure all reasoning steps occur
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- type: trace-span-count
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value:
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pattern: 'reasoning_*'
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min: 3
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# Ensure the LLM generation span exists
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- type: trace-span-count
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value:
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pattern: 'llm_generation'
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min: 1
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# Ensure the overall workflow completes quickly
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- type: trace-span-duration
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value:
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pattern: 'rag_agent_workflow'
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max: 5000 # 5 seconds max
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# Ensure no errors occur
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- type: trace-error-spans
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value:
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max_count: 0
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- vars:
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topic: 'machine learning'
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metadata:
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tracingEnabled: true
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testCaseId: 'python-test-2'
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assert:
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- type: trace-span-count
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value:
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pattern: 'rag_agent_workflow'
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min: 1
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max: 1
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- type: trace-span-count
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value:
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pattern: 'retrieve_document_*'
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min: 3
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max: 3
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- type: trace-span-count
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value:
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pattern: 'reasoning_*'
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min: 3
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- type: trace-span-duration
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value:
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pattern: 'rag_agent_workflow'
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max: 5000
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- type: trace-error-spans
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value:
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max_count: 0
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# Default assertions for all test cases
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defaultTest:
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assert:
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# Monitor overall latency
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- type: trace-span-duration
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value:
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pattern: '*'
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max: 2000
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percentile: 95
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weight: 0
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metric: p95_latency
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# Ensure retrieval operations are fast
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- type: trace-span-duration
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value:
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pattern: 'retrieve_document_*'
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max: 500
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# Tracing configuration - note we accept both JSON and protobuf
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tracing:
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enabled: true
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otlp:
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http:
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enabled: true
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port: 4318
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# Python's OTLP exporter uses protobuf by default
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acceptFormats: ['json', 'protobuf']
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