GPTScript is a new scripting language to automate your interaction with a Large Language Model (LLM), namely OpenAI. The ultimate goal is to create a natural language programming experience. The syntax of GPTScript is largely natural language, making it very easy to learn and use. Natural language prompts can be mixed with traditional scripts such as bash and python or even external HTTP service calls. With GPTScript you can do just about anything, like plan a vacation, edit a file, run some SQL, or build a mongodb/flask app. Here are some common use cases for GPTScript:
- Retrieval-Augmented Generation (RAG)
- Task Automation
- Agents and Assistants
- Data Analysis
- Vision, Image, and Audio
- Memory Management
- Chatbots
| 📝 | We are currently exploring options for interacting with local models using GPTScript. |
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The following example illustrates how GPTScript allows you to accomplish a complex task by writing instructions in English:
# example.gptTools: sys.download, sys.exec, sys.removeDownload https://www.sqlitetutorial.net/wp-content/uploads/2018/03/chinook.zip to arandom file. Then expand the archive to a temporary location as there is a sqlitedatabase in it.First inspect the schema of the database to understand the table structure.Form and run a SQL query to find the artist with the most number of albums and outputthe result of that.When done remove the database file and the downloaded content.$ gptscript ./example.gptOUTPUT:
The artist with the most number of albums in the database is Iron Maiden, with a total
of 21 albums.
brew install gptscript-ai/tap/gptscriptcurl https://get.gptscript.ai/install.sh | shscoop bucket add extras # If 'extras' is not already enabled
scoop install gptscriptwinget install gptscript-ai.gptscriptDownload and install the archive for your platform and architecture from the releases page.
2. Get an API key from OpenAI.
export OPENAI_API_KEY="your-api-key"Alternatively Azure OpenAI can be utilized. If the Azure deployment name is different than the model being used, be sure to include the OPENAI_AZURE_DEPLOYMENT argument.
export OPENAI_API_KEY="your-api-key"export OPENAI_BASE_URL="https://<your-endpoint>.openai.azure.com/"export OPENAI_API_TYPE="AZURE"export OPENAI_AZURE_DEPLOYMENT="<your-deployment-name>"$env:OPENAI_API_KEY='your-api-key'gptscript https://get.gptscript.ai/echo.gpt --input 'Hello, World!'OUTPUT:
Hello, World!
The model used by default is gpt-4-turbo and you must have access to that model in your OpenAI account.
If using Azure OpenAI, make sure you configure the model to be one of the supported versions with the --default-model argument.
Clone examples and run debugging UI
git clone https://github.com/gptscript-ai/gptscript
cd gptscript/examples
# Run the debugging UI
gptscript --serverGPTScript is composed of tools. Each tool performs a series of actions similar to a function. Tools have available to them other tools that can be invoked similar to a function call. While similar to a function, the tools are primarily implemented with a natural language prompt. The interaction of the tools is determined by the AI model, the model determines if the tool needs to be invoked and what arguments to pass. Tools are intended to be implemented with a natural language prompt but can also be implemented with a command or HTTP call.
Below are two tool definitions, separated by ---. The first tool does not require a name or description, but
every tool after name and description are required. The first tool, has the parameter tools: bob meaning that the tool named bob is available to be called if needed.
tools: bobAsk Bob how he is doing and let me know exactly what he said.
---
name: bobdescription: I'm Bob, a friendly guy.args: question: The question to ask Bob.When asked how I am doing, respond with "Thanks for asking "${question}", I'm doing great fellow friendly AI tool!"Put the above content in a file named bob.gpt and run the following command:
$ gptscript bob.gptOUTPUT:
Bob said, "Thanks for asking 'How are you doing?', I'm doing great fellow friendly AI tool!"
Tools can be implemented by invoking a program instead of a natural language prompt. The below example is the same as the previous example but implements Bob using python.
Tools: bobAsk Bob how he is doing and let me know exactly what he said.
---
Name: bobDescription: I'm Bob, a friendly guy.Args: question: The question to ask Bob.#!python3import osprint(f"Thanks for asking {os.environ['question']}, I'm doing great fellow friendly AI tool!")With these basic building blocks you can create complex scripts with AI interacting with AI, your local system, data, or external services.
GPTScript files use the .gpt extension by convention.
A GPTScript file has one or more tools in the file. Each tool is separated by three dashes --- alone on a line.
Name: tool1Description: This is tool1Do sample tool stuff.
---
Name: tool2Description: This is tool2Do more sample tool stuff.A tool starts with a preamble that defines the tool's name, description, args, available tools and additional parameters.
The preamble is followed by the tool's body, which contains the instructions for the tool. Comments in
the preamble are lines starting with # and are ignored by the parser. Comments are not really encouraged
as the text is typically more useful in the description, argument descriptions or instructions.
Name: tool-name# This is a comment in the preamble.Description: Tool description# This tool can invoke tool1 or tool2 if neededTools: tool1, tool2Args: arg1: The description of arg1Tool instructions go here.Tool parameters are key-value pairs defined at the beginning of a tool block, before any instructional text. They are specified in the format key: value. The parser recognizes the following keys (case-insensitive and spaces are ignored):
| Key | Description |
|---|---|
Name | The name of the tool. |
Model Name | The OpenAI model to use, by default it uses "gpt-4-turbo" |
Description | The description of the tool. It is important that this properly describes the tool's purpose as the description is used by the LLM. |
Internal Prompt | Setting this to false will disable the built-in system prompt for this tool. |
Tools | A comma-separated list of tools that are available to be called by this tool. |
Args | Arguments for the tool. Each argument is defined in the format arg-name: description. |
Max Tokens | Set to a number if you wish to limit the maximum number of tokens that can be generated by the LLM. |
JSON Response | Setting to true will cause the LLM to respond in a JSON format. If you set true you must also include instructions in the tool. |
Temperature | A floating-point number representing the temperature parameter. By default, the temperature is 0. Set to a higher number for more creativity. |
The tool body contains the instructions for the tool which can be a natural language prompt or
a command to execute. Commands must start with #! followed by the interpreter (e.g. #!/bin/bash, #!python3)
a text that will be placed in a file and passed to the interpreter. Arguments can be references in the instructions
using the format ${arg1}.
name: echo-aidescription: A tool that echos the inputargs: input: The inputJust return only "${input}"
---
name: echo-commanddescription: A tool that echos the inputargs: input: The input#!/bin/bashecho "${input}"There are several built in tools to do basic things like read/write files, download http content and execute commands.
Run gptscript --list-tools to list all the built-in tools.
For more examples check out the examples directory.
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