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100 lines
2.8 KiB
Markdown
100 lines
2.8 KiB
Markdown
# Customer Intent Extraction
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This sample is using OpenAI chat model(ChatGPT/GPT4) to identify customer intent from customer's question.
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By going through this sample you will learn how to create a flow from existing working code (written in LangChain in this case).
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This is the [existing code](./intent.py).
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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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Ensure you have put your azure OpenAI endpoint key in .env file.
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```bash
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cat .env
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```
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## Run flow
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1. init flow directory - create promptflow folder from existing python file
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```bash
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pf flow init --flow . --entry intent.py --function extract_intent --prompt-template chat_prompt=user_intent_zero_shot.jinja2
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```
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The generated files:
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- extract_intent_tool.py: Wrap the func `extract_intent` in the `intent.py` script into a [Python Tool](https://promptflow.azurewebsites.net/tools-reference/python-tool.html).
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- flow.dag.yaml: Describes the DAG(Directed Acyclic Graph) of this flow.
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- .gitignore: File/folder in the flow to be ignored.
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2. create needed custom connection
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```bash
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pf connection create -f .env --name custom_connection
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```
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3. test flow with single line input
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```bash
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pf flow test --flow . --inputs ./data/sample.json
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```
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4. run with multiple lines input
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```bash
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pf run create --flow . --data ./data --column-mapping history='${data.history}' customer_info='${data.customer_info}'
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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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5. list/show
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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 completed run name
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name=$(pf run list | jq '.[] | select(.name | contains("customer_intent_extraction")) | .name'| head -n 1 | tr -d '"')
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# show run
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pf run show --name $name
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# show specific run detail, top 3 lines
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pf run show-details --name $name -r 3
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```
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6. evaluation
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```bash
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# create evaluation run
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pf run create --flow ../../evaluation/eval-classification-accuracy --data ./data --column-mapping groundtruth='${data.intent}' prediction='${run.outputs.output}' --run $name
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```
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```bash
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# get the evaluation run in previous step
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eval_run_name=$(pf run list | jq '.[] | select(.name | contains("eval_classification_accuracy")) | .name'| head -n 1 | tr -d '"')
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# show run
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pf run show --name $eval_run_name
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# show run output
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pf run show-details --name $eval_run_name -r 3
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```
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6. visualize
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```bash
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# visualize in browser
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pf run visualize --name $eval_run_name # your evaluation run name
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```
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## Deploy
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### Serve as a local test app
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```bash
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pf flow serve --source . --port 5123 --host localhost
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```
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Visit http://localhost:5213 to access the test app.
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### Export
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#### Export as docker
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```bash
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# pf flow export --source . --format docker --output ./package
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``` |