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34 行
1.3 KiB
Markdown
34 行
1.3 KiB
Markdown
# Perceived Intelligence Evaluation
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This is a flow leverage llm to eval perceived intelligence.
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Perceived intelligence is the degree to which a bot can impress the user with its responses, by showing originality, insight, creativity, knowledge, and adaptability.
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Tools used in this flow：
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- `python` tool
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- built-in `llm` tool
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### 0. Setup connection
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Prepare your Azure OpenAI resource follow this [instruction](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal) and get your `api_key` if you don't have one.
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```bash
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# Override keys with --set to avoid yaml file changes
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pf connection create --file ../../../connections/azure_openai.yml --set api_key=<your_api_key> api_base=<your_api_base>
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```
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### 1. Test flow/node
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```bash
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# test with default input value in flow.dag.yaml
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pf flow test --flow .
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```
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### 2. create flow run with multi line data
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```bash
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pf run create --flow . --data ./data.jsonl --column-mapping question='${data.question}' answer='${data.answer}' context='${data.context}' --stream
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```
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You can also skip providing `column-mapping` if provided data has same column name as the flow.
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Reference [here](https://aka.ms/pf/column-mapping) for default behavior when `column-mapping` not provided in CLI.
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