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343 lines
8.5 KiB
YAML
343 lines
8.5 KiB
YAML
$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
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inputs:
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question:
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type: string
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default: Which tent is the most waterproof?
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is_chat_input: false
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answer:
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type: string
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default: The Alpine Explorer Tent is the most waterproof.
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is_chat_input: false
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context:
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type: string
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default: From the our product list, the alpine explorer tent is the most
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waterproof. The Adventure Dining Tabbe has higher weight.
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is_chat_input: false
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ground_truth:
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type: string
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default: The Alpine Explorer Tent has the highest rainfly waterproof rating at 3000m
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is_chat_input: false
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metrics:
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type: string
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default: grounding,answer_relevance,answer_quality,context_precision,answer_similarity,creativity,context_recall,answer_correctness
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is_chat_input: false
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outputs:
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answer_correctness:
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type: string
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reference: ${concat_scores.output.answer_correctness}
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context_recall:
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type: string
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reference: ${concat_scores.output.context_recall}
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answer_similarity:
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type: string
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reference: ${concat_scores.output.answer_similarity}
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answer_relevance:
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type: string
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reference: ${concat_scores.output.answer_relevance}
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context_precision:
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type: string
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reference: ${concat_scores.output.context_precision}
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creativity:
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type: string
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reference: ${concat_scores.output.creativity}
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grounding:
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type: string
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reference: ${concat_scores.output.grounding}
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answer_quality:
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type: string
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reference: ${concat_scores.output.answer_quality}
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nodes:
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- name: select_metrics
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type: python
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source:
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type: code
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path: select_metrics.py
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inputs:
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metrics: ${inputs.metrics}
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use_variants: false
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- name: validate_input
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type: python
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source:
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type: code
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path: validate_input.py
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inputs:
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answer: ${inputs.answer}
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context: ${inputs.context}
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ground_truth: ${inputs.ground_truth}
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question: ${inputs.question}
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selected_metrics: ${select_metrics.output}
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use_variants: false
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- name: grounding
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type: llm
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source:
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type: code
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path: grounding.jinja2
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inputs:
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deployment_name: gpt-4
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temperature: 0
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top_p: 1
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presence_penalty: 0
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frequency_penalty: 0
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answer: ${inputs.answer}
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context: ${inputs.context}
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provider: AzureOpenAI
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connection: open_ai_connection
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api: chat
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module: promptflow.tools.aoai
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activate:
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when: ${validate_input.output.grounding}
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is: true
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use_variants: false
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- name: answer_quality
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type: llm
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source:
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type: code
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path: answer_quality.jinja2
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inputs:
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deployment_name: gpt-4
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temperature: 0
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top_p: 1
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presence_penalty: 0
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frequency_penalty: 0
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answer: ${inputs.answer}
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question: ${inputs.question}
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provider: AzureOpenAI
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connection: open_ai_connection
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api: chat
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module: promptflow.tools.aoai
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activate:
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when: ${validate_input.output.answer_quality}
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is: true
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use_variants: false
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- name: answer_similarity
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type: llm
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source:
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type: code
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path: answer_similarity.jinja2
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inputs:
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deployment_name: gpt-4
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temperature: 0
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top_p: 1
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presence_penalty: 0
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frequency_penalty: 0
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answer: ${inputs.answer}
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ground_truth: ${inputs.ground_truth}
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question: ${inputs.question}
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provider: AzureOpenAI
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connection: open_ai_connection
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api: chat
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module: promptflow.tools.aoai
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activate:
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when: ${validate_input.output.answer_similarity}
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is: true
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use_variants: false
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- name: creativity
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type: llm
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source:
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type: code
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path: creativity.jinja2
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inputs:
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deployment_name: gpt-4
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temperature: 0
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top_p: 1
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presence_penalty: 0
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frequency_penalty: 0
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answer: ${inputs.answer}
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question: ${inputs.question}
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provider: AzureOpenAI
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connection: open_ai_connection
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api: chat
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module: promptflow.tools.aoai
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activate:
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when: ${validate_input.output.creativity}
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is: true
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use_variants: false
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- name: context_recall
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type: llm
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source:
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type: code
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path: context_recall.jinja2
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inputs:
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deployment_name: gpt-4
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temperature: 0
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top_p: 1
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presence_penalty: 0
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frequency_penalty: 0
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context: ${inputs.context}
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ground_truth: ${inputs.ground_truth}
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question: ${inputs.question}
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provider: AzureOpenAI
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connection: open_ai_connection
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api: chat
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module: promptflow.tools.aoai
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activate:
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when: ${validate_input.output.context_recall}
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is: true
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use_variants: false
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- name: calculate_context_recall
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type: python
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source:
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type: code
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path: calculate_context_recall.py
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inputs:
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llm_result: ${context_recall.output}
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activate:
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when: ${validate_input.output.context_recall}
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is: true
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use_variants: false
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- name: context_precision
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type: llm
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source:
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type: code
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path: context_precision.jinja2
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inputs:
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deployment_name: gpt-4
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temperature: 0
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top_p: 1
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presence_penalty: 0
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frequency_penalty: 0
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context: ${inputs.context}
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ground_truth: ${inputs.ground_truth}
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question: ${inputs.question}
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provider: AzureOpenAI
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connection: open_ai_connection
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api: chat
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module: promptflow.tools.aoai
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activate:
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when: ${validate_input.output.context_precision}
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is: true
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use_variants: false
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- name: answer_relevance
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type: llm
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source:
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type: code
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path: answer_relevance.jinja2
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inputs:
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deployment_name: gpt-4
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temperature: 0
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top_p: 1
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presence_penalty: 0
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frequency_penalty: 0
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answer: ${inputs.answer}
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context: ${inputs.context}
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provider: AzureOpenAI
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connection: open_ai_connection
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api: chat
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module: promptflow.tools.aoai
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activate:
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when: ${validate_input.output.answer_relevance}
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is: true
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use_variants: false
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- name: handle_generated_question
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type: python
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source:
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type: code
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path: handle_generated_question.py
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inputs:
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llm_result: ${answer_relevance.output}
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activate:
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when: ${validate_input.output.answer_relevance}
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is: true
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use_variants: false
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- name: embedding_question
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type: python
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source:
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type: package
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tool: promptflow.tools.embedding.embedding
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inputs:
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connection: open_ai_connection
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deployment_name: text-embedding-ada-002
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input: ${inputs.question}
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activate:
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when: ${validate_input.output.answer_relevance}
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is: true
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use_variants: false
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- name: embedding_generated_question
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type: python
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source:
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type: package
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tool: promptflow.tools.embedding.embedding
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inputs:
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connection: open_ai_connection
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deployment_name: text-embedding-ada-002
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input: ${handle_generated_question.output.question}
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activate:
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when: ${validate_input.output.answer_relevance}
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is: true
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use_variants: false
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- name: calculate_answer_relevance
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type: python
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source:
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type: code
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path: calculate_answer_relevance.py
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inputs:
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generated_question_embedding: ${embedding_generated_question.output}
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noncommittal: ${handle_generated_question.output.noncommittal}
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question_embedding: ${embedding_question.output}
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activate:
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when: ${validate_input.output.answer_relevance}
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is: true
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use_variants: false
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- name: answer_correctness
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type: llm
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source:
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type: code
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path: answer_correctness.jinja2
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inputs:
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deployment_name: gpt-4
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temperature: 0
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top_p: 1
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presence_penalty: 0
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frequency_penalty: 0
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answer: ${inputs.answer}
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ground_truth: ${inputs.ground_truth}
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question: ${inputs.question}
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provider: AzureOpenAI
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connection: open_ai_connection
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api: chat
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module: promptflow.tools.aoai
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activate:
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when: ${validate_input.output.answer_correctness}
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is: true
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use_variants: false
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- name: calculate_answer_correctness
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type: python
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source:
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type: code
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path: calculate_answer_correctness.py
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inputs:
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similarity_score: ${answer_similarity.output}
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statement_result: ${answer_correctness.output}
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activate:
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when: ${validate_input.output.answer_correctness}
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is: true
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use_variants: false
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- name: concat_scores
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type: python
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source:
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type: code
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path: concat_scores.py
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inputs:
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answer_correctness: ${calculate_answer_correctness.output}
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answer_quality: ${answer_quality.output}
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answer_relevance: ${calculate_answer_relevance.output}
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answer_similarity: ${answer_similarity.output}
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context_precision: ${context_precision.output}
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context_recall: ${calculate_context_recall.output}
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creativity: ${creativity.output}
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grounding: ${grounding.output}
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use_variants: false
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- name: aggregate_results
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type: python
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source:
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type: code
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path: aggregate.py
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inputs:
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metrics: ${inputs.metrics}
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results: ${concat_scores.output}
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aggregation: true
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use_variants: false
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node_variants: {}
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environment:
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python_requirements_txt: requirements.txt
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