microsoft--promptflow/examples/flows/standard/gen-docstring

Generate Python docstring

This example can help you automatically generate Python code's docstring and return the modified code.

Tools used in this flow：

• load_code tool, it can load code from a file path.
  • Load content from a local file.
  • Loading content from a remote URL, currently loading HTML content, not just code.
• divide_code tool, it can divide code into code blocks.
  • To avoid files that are too long and exceed the token limit, it is necessary to split the file.
  • Avoid using the same function (such as init(self)) to generate docstrings in the same one file, which may cause confusion when adding docstrings to the corresponding functions in the future.
• generate_docstring tool, it can generate docstring for a code block, and merge docstring into origin code.

What you will learn

In this flow, you will learn

• How to compose an auto generate docstring flow.
• How to use different LLM APIs to request LLM, including synchronous/asynchronous APIs, chat/completion APIs.
• How to use asynchronous multiple coroutine approach to request LLM API.
• How to construct a prompt.

Prerequisites

Install promptflow sdk and other dependencies:

pip install -r requirements.txt

Create connection for LLM to use

# Override keys with --set to avoid yaml file changes
pf connection create --file ../../../connections/azure_openai.yml --set api_key=<your_api_key> api_base=<your_api_base>

Note:
The azure_openai.yml file is located in connections folder.
We are using connection named open_ai_connectionin flow.dag.yaml.

Execute with Promptflow

Execute with SDK

python main.py --source <your_file_path>
Note: the file path should be a python file path, default is ./azure_open_ai.py.

A webpage will be generated, displaying diff:

Execute with CLI

# run flow with default file path in flow.dag.yaml
pf flow test --flow . 

# run flow with file path
pf flow test --flow . --inputs source="./azure_open_ai.py"

# run flow with batch data
pf run create --flow . --data ./data.jsonl --name auto_generate_docstring --column-mapping source='${data.source}'

Output the code after add the docstring.

You can also skip providing column-mapping if provided data has same column name as the flow. Reference here for default behavior when column-mapping not provided in CLI.