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‎LICENSE

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Apache License
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‎README.md

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# GPTScript Python Module
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## Introduction
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The GPTScript Python module is a library that provides a simple interface to create and run gptscripts within Python applications, and Jupyter notebooks. It allows you to define tools, execute them, and process the responses.
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## Installation
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You can install the GPTScript Python module using pip.
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```bash
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pip install gptscript
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```
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You will also want to have the tool download and install the gptscript cli by running:
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```bash
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install_gptscript
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```
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The script is added to the same bin directory as the python executable, so it should be in your path.
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Or you can install the gptscript cli from your code by running:
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```python
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from gptscript.install import install
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install()
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```
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## Using the Module
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The module requires the OPENAI_API_KEY environment variable to be set with your OPENAI key. You can set it in your shell or in your code.
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```bash
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export OPENAI_AI_KEY="your-key"
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```
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## Tools
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The `Tool` class represents a gptscript tool. The fields align with what you would be able to define in a normal gptscript .gpt file.
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### Fields
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- `name`: The name of the tool.
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- `description`: A description of the tool.
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- `tools`: Additional tools associated with the main tool.
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- `max_tokens`: The maximum number of tokens to generate.
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- `model`: The GPT model to use.
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- `cache`: Whether to use caching for responses.
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- `temperature`: The temperature parameter for response generation.
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- `args`: Additional arguments for the tool.
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- `internal_prompt`: Internal prompt for the tool.
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- `instructions`: Instructions or additional information about the tool.
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- `json_response`: Whether the response should be in JSON format.(If you set this to True, you must say 'json' in the instructions as well.)
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## Primary Functions
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Aside from the list methods there are `exec` and `exec_file` methods that allow you to execute a tool and get the responses. Those functions also provide a streaming version of execution if you want to process the output streams in your code as the tool is running.
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### `list_models()`
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This function lists the available GPT models.
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```python
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from gptscript.command import list_models
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models = list_models()
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print(models)
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```
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### `list_tools()`
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This function lists the available tools.
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```python
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from gptscript.command import list_tools
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tools = list_tools()
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print(tools)
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```
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### `exec(tool)`
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This function executes a tool and returns the response.
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```python
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from gptscript.command import exec, complex_tool
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response = exec(complex_tool())
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print(response)
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```
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### `exec_file(tool_path)`
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This function executes a tool from a file and returns the response.
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```python
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from gptscript.command import exec_file
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response = exec_file("./init.gpt")
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print(response)
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```
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### `stream_exec(tool)`
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This function streams the execution of a tool and returns the output, error, and process wait function.
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```python
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from gptscript.command import stream_exec, complex_tool
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out, err, wait = stream_exec(complex_tool())
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print(out)
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print(err)
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wait()
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```
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### `stream_exec_file(tool_path)`
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This function streams the execution of a tool from a file and returns the output, error, and process wait function.
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```python
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from gptscript.command import stream_exec_file
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out, err, wait = stream_exec_file("./init.gpt")
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print(out)
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print(err)
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wait()
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```
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## Example Usage
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```python
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from gptscript.tool import Tool
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# Define a simple tool
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simple_tool = Tool(
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instructions="""
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This tool is used to generate a simple prompt
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"""
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)
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# Define a complex tool
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complex_tool = Tool(
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tools="sys.write",
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json_response=True,
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cache=False,
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instructions="""
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Create three short graphic artist descriptions and their muses.
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These should be descriptive and explain their point of view.
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Also come up with a made-up name, they each should be from different
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backgrounds and approach art differently.
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the response format should be:
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{
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artists: [{
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name: "name"
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description: "description"
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}]
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}
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"""
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)
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# Execute the complex tool
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response = exec(complex_tool)
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print(response)
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```

‎gptscript/__init__.py

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