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112 lines
4.7 KiB
Markdown
112 lines
4.7 KiB
Markdown
# Python
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## Introduction
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Users are empowered by the Python Tool to offer customized code snippets as self-contained executable nodes in PromptFlow.
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Users can effortlessly create Python tools, edit code, and verify results with ease.
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## Inputs
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| Name | Type | Description | Required |
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|--------|--------|------------------------------------------------------|---------|
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| Code | string | Python code snippet | Yes |
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| Inputs | - | List of tool function parameters and its assignments | - |
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### Types
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| Type | Python example | Description |
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|-----------------------------------------------------|---------------------------------|--------------------------------------------|
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| int | param: int | Integer type |
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| bool | param: bool | Boolean type |
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| string | param: str | String type |
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| double | param: float | Double type |
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| list | param: list or param: List[T] | List type |
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| object | param: dict or param: Dict[K, V] | Object type |
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| [Connection](../../concepts/concept-connections.md) | param: CustomConnection | Connection type, will be handled specially |
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Parameters with `Connection` type annotation will be treated as connection inputs, which means:
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- Promptflow extension will show a selector to select the connection.
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- During execution time, promptflow will try to find the connection with the name same from parameter value passed in.
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Note that `Union[...]` type annotation is supported **ONLY** for connection type,
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for example, `param: Union[CustomConnection, OpenAIConnection]`.
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## Outputs
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The return of the python tool function.
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## How to write Python Tool?
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### Guidelines
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1. Python Tool Code should consist of a complete Python code, including any necessary module imports.
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2. Python Tool Code must contain a function decorated with @tool (tool function), serving as the entry point for execution. The @tool decorator should be applied only once within the snippet.
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_Below sample defines python tool "my_python_tool", decorated with @tool_
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3. Python tool function parameters must be assigned in 'Inputs' section
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_Below sample defines inputs "message" and assign with "world"_
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4. Python tool function shall have return
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_Below sample returns a concatenated string_
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### Code
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The snippet below shows the basic structure of a tool function. Promptflow will read the function and extract inputs
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from function parameters and type annotations.
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```python
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from promptflow.core import tool
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from promptflow.connections import CustomConnection
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# The inputs section will change based on the arguments of the tool function, after you save the code
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# Adding type to arguments and return value will help the system show the types properly
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# Please update the function name/signature per need
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@tool
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def my_python_tool(message: str, my_conn: CustomConnection) -> str:
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my_conn_dict = dict(my_conn)
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# Do some function call with my_conn_dict...
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return 'hello ' + message
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```
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### Inputs
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| Name | Type | Sample Value in Flow Yaml | Value passed to function|
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|---------|--------|-------------------------| ------------------------|
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| message | string | "world" | "world" |
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| my_conn | CustomConnection | "my_conn" | CustomConnection object |
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Promptflow will try to find the connection named 'my_conn' during execution time.
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### outputs
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```python
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"hello world"
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```
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### Keyword Arguments Support
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Starting from version 1.0.0 of PromptFlow and version 1.4.0 of [Prompt flow for VS Code](https://marketplace.visualstudio.com/items?itemName=prompt-flow.prompt-flow),
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we have introduced support for keyword arguments (kwargs) in the Python tool.
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```python
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from promptflow.core import tool
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@tool
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def print_test(normal_input: str, **kwargs):
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for key, value in kwargs.items():
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print(f"Key {key}'s value is {value}")
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return len(kwargs)
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```
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When you add `kwargs` in your python tool like above code, you can insert variable number of inputs by the `+Add input` button.
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