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75 lines
2.2 KiB
Markdown
75 lines
2.2 KiB
Markdown
# Basic flow with builtin llm tool
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A basic standard flow that calls Azure OpenAI with builtin llm tool.
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Tools used in this flow：
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- `prompt` tool
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- built-in `llm` tool
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Connections used in this flow:
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- `azure_open_ai` connection
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## Prerequisites
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Install promptflow sdk and other dependencies:
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```bash
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pip install -r requirements.txt
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```
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## 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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Note in this example, we are using [chat api](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/chatgpt?pivots=programming-language-chat-completions), please use `gpt-35-turbo` or `gpt-4` model deployment.
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Ensure you have created `open_ai_connection` connection before.
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```bash
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pf connection show -n open_ai_connection
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```
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Create connection if you haven't done that. Ensure you have put your azure OpenAI endpoint key in [azure_openai.yml](../../../connections/azure_openai.yml) file.
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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 -f ../../../connections/azure_openai.yml --name open_ai_connection --set api_key=<your_api_key> api_base=<your_api_base>
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```
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## Run flow
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### Run with single line input
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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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# test with inputs
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pf flow test --flow . --inputs text="Python Hello World!"
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```
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### run with multiple lines data
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- create run
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```bash
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pf run create --flow . --data ./data.jsonl --column-mapping text='${data.text}' --stream
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```
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You can also skip providing `column-mapping` if provided data has same column name as the flow.
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Reference [here](https://aka.ms/pf/column-mapping) for default behavior when `column-mapping` not provided in CLI.
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- list and show run meta
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```bash
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# list created run
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pf run list
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# get a sample run name
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name=$(pf run list -r 10 | jq '.[] | select(.name | contains("basic_with_builtin_llm")) | .name'| head -n 1 | tr -d '"')
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# show specific run detail
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pf run show --name $name
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# show output
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pf run show-details --name $name
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# visualize run in browser
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pf run visualize --name $name
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```
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