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Signed-off-by: Grant Linville <[email protected]> Co-authored-by: Donnie Adams <[email protected]>
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# Workspace | ||
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One concept in GPTScript is the workspace directory. | ||
This is a directory meant to be used by tools that need to interact with the local file system. | ||
By default, the workspace directory is a one-off temporary directory. | ||
The workspace directory can be set with the `--workspace` argument when running GPTScript, like this: | ||
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```bash | ||
gptscript --workspace . my-script.gpt | ||
``` | ||
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In the above example, the user’s current directory (denoted by `.`) will be set as the workspace. | ||
The workspace directory is no longer temporary if it is explicitly set, and everything in it will persist after the script has finished running. | ||
Both absolute and relative paths are supported. | ||
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Regardless of whether it is set implicitly or explicitly, the workspace is then made available to the script execution as the `GPTSCRIPT_WORKSPACE_DIR` environment variable. | ||
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:::info | ||
GPTScript does not force scripts or tools to write to, read from, or otherwise use the workspace. | ||
The tools must decide to make use of the workspace environment variable. | ||
::: | ||
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## The Workspace Context Tool | ||
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To make a non-code tool that uses the LLM aware of the workspace, you can reference the workspace context tool: | ||
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``` | ||
Context: github.com/gptscript-ai/context/workspace | ||
``` | ||
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This tells the LLM (by way of a [system message](https://platform.openai.com/docs/guides/text-generation/chat-completions-api)) what the workspace directory is, | ||
what its initial contents are, and that if it decides to create a file or directory, it should do so in the workspace directory. | ||
This will not, however, have any impact on code-based tools (i.e. Python, Bash, or Go tools). | ||
Such tools will have the `GPTSCRIPT_WORKSPACE_DIR` environment variable available to them, but they must be written in such a way that they make use of it. | ||
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This context tool also automatically shares the `sys.ls`, `sys.read`, and `sys.write` tools with the tool that is using it as a context. | ||
This is because if a tool intends to interact with the workspace, it minimally needs these tools. |
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# Code Tool Guidelines | ||
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GPTScript can handle the packaging and distribution of code-based tools via GitHub repos. | ||
For more information on how this works, see the [authoring guide](02-authoring.md#sharing-tools). | ||
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This guide provides guidelines for setting up GitHub repos for proper tool distribution. | ||
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## Common Guidelines | ||
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### `tool.gpt` or `agent.gpt` file | ||
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Every repo should have a `tool.gpt` or `agent.gpt` file. This is the main logic of the tool. | ||
If both files exist, GPTScript will use the `agent.gpt` file and ignore the `tool.gpt` file. | ||
Your repo can have other `.gpt` files that are referenced by the main file, but there must be a `tool.gpt` or `agent.gpt` file present. | ||
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Under most circumstances, this file should live in the root of the repo. | ||
If you are using a single repo for the distribution of multiple tools (see [gptscript-ai/context](https://github.com/gptscript-ai/context) for an example), | ||
then you can have the `tool.gpt`/`agent.gpt` file in a subdirectory, and the tool will now be able to be referenced as `github.com/<user>/<repo>/<subdirectory>`. | ||
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### Name and Description directives | ||
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We recommend including a `Name` and `Description` directive for your tool. | ||
This is useful for both people and LLMs to understand what the tool will do and when to use it. | ||
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### Parameters | ||
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Any parameters specified in the tool will be available as environment variables in your code. | ||
We recommend handling parameters that way, rather than using command-line arguments. | ||
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## Python Guidelines | ||
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### Calling Python in the tool body | ||
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The body of the `tool.gpt`/`agent.gpt` file needs to call Python. This can be done as an inline script like this: | ||
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``` | ||
Name: my-python-tool | ||
#!python3 | ||
print('hello world') | ||
``` | ||
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An inline script like this is only recommended for simple use cases that don't need external dependencies. | ||
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If your use case is more complex or requires external dependencies, you can reference a Python script in your repo, like this: | ||
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``` | ||
Name: my-python-tool | ||
#!/usr/bin/env python3 ${GPTSCRIPT_TOOL_DIR}/tool.py | ||
``` | ||
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(This example assumes that your entrypoint to your Python program is in a file called `tool.py`. You can call it what you want.) | ||
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### `requirements.txt` file | ||
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If your Python program needs any external dependencies, you can create a `requirements.txt` file at the same level as | ||
your `tool.gpt`/`agent.gpt` file. GPTScript will handle downloading the dependencies before it runs the tool. | ||
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The file structure should look something like this: | ||
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``` | ||
. | ||
├── requirements.txt | ||
├── tool.py | ||
└── tool.gpt | ||
``` | ||
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## JavaScript (Node.js) Guidelines | ||
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### Calling Node.js in the tool body | ||
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The body of the `tool.gpt`/`agent.gpt` file needs to call Node. This can be done as an inline script like this: | ||
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``` | ||
Name: my-node-tool | ||
#!node | ||
console.log('hello world') | ||
``` | ||
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An inline script like this is only recommended for simple use cases that don't need external dependencies. | ||
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If your use case is more complex or requires external dependencies, you can reference a Node script in your repo, like this: | ||
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``` | ||
Name: my-node-tool | ||
#!/usr/bin/env node ${GPTSCRIPT_TOOL_DIR}/tool.js | ||
``` | ||
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(This example assumes that your entrypoint to your Node program is in a file called `tool.js`. You can call it what you want.) | ||
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### `package.json` file | ||
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If your Node program needs any external dependencies, you can create a `package.json` file at the same level as | ||
your `tool.gpt`/`agent.gpt` file. GPTScript will handle downloading the dependencies before it runs the tool. | ||
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The file structure should look something like this: | ||
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``` | ||
. | ||
├── package.json | ||
├── tool.js | ||
└── tool.gpt | ||
``` | ||
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## Go Guidelines | ||
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GPTScript does not support inline code for Go, so you must call to an external program from the tool body like this: | ||
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``` | ||
Name: my-go-tool | ||
#!${GPTSCRIPT_TOOL_DIR}/bin/gptscript-go-tool | ||
``` | ||
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:::important | ||
Unlike the Python and Node cases above where you can name the file anything you want, Go tools must be `#!${GPTSCRIPT_TOOL_DIR}/bin/gptscript-go-tool`. | ||
::: | ||
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GPTScript will build the Go program located at `./main.go` to a file called `./bin/gptscript-go-tool` before running the tool. | ||
All of your dependencies need to be properly specified in a `go.mod` file. | ||
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The file structure should look something like this: | ||
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``` | ||
. | ||
├── go.mod | ||
├── go.sum | ||
├── main.go | ||
└── tool.gpt | ||
``` |
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# Daemon Tools (Advanced) | ||
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One advanced use case that GPTScript supports is daemon tools. | ||
A daemon tool is a tool that starts a long-running HTTP server in the background, that will continue running until GPTScript is done executing. | ||
Other tools can easily send HTTP POST requests to the daemon tool. | ||
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## Example | ||
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Here is an example of a daemon tool with a simple echo server written in an inline Node.js script: | ||
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``` | ||
Tools: my-daemon | ||
Param: first: the first parameter | ||
Param: second: the second parameter | ||
#!http://my-daemon.daemon.gptscript.local/myPath | ||
--- | ||
Name: my-daemon | ||
#!sys.daemon node | ||
const http = require('http'); | ||
const server = http.createServer((req, res) => { | ||
if (req.method === 'GET' || req.method === 'POST') { | ||
// Extract the path from the request URL | ||
const path = req.url; | ||
let body = ''; | ||
req.on('data', chunk => { | ||
body += chunk.toString(); | ||
}) | ||
// Respond with the path and body | ||
req.on('end', () => { | ||
res.writeHead(200, { 'Content-Type': 'text/plain' }); | ||
res.write(`Body: ${body}\n`); | ||
res.end(`Path: ${path}`); | ||
}) | ||
} else { | ||
res.writeHead(405, { 'Content-Type': 'text/plain' }); | ||
res.end('Method Not Allowed'); | ||
} | ||
}); | ||
const PORT = process.env.PORT || 3000; | ||
server.listen(PORT, () => { | ||
console.log(`Server is listening on port ${PORT}`); | ||
}); | ||
``` | ||
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Let's talk about the daemon tool, called `my-daemon`, first. | ||
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### The Daemon Tool | ||
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The body of this tool begins with `#!sys.daemon`. This tells GPTScript to take the rest of the body as a command to be | ||
run in the background that will listen for HTTP requests. GPTScript will run this command (in this case, a Node script). | ||
GPTScript will assign a port number for the server and set the `PORT` environment variable to that number, so the | ||
server needs to check that variable and listen on the proper port. | ||
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After GPTScript runs the daemon, it will send it an HTTP GET request to make sure that it is running properly. | ||
The daemon needs to respond with a 200 OK to this request. | ||
By default, the request goes to `/`, but this can be configured with the following syntax: | ||
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``` | ||
#!sys.daemon (path=/api/ready) node | ||
// (node script here) | ||
``` | ||
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### The Entrypoint Tool | ||
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The entrypoint tool at the top of this script sends an HTTP request to the daemon tool. | ||
There are a few important things to note here: | ||
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- The `Tools: my-daemon` directive is needed to show that this tool requires the `my-daemon` tool to already be running. | ||
- When the entrypoint tool runs, GPTScript will check if `my-daemon` is already running. If it is not, GPTScript will start it. | ||
- The `#!http://my-daemon.daemon.gptscript.local/myPath` in the body tells GPTScript to send an HTTP request to the daemon tool. | ||
- The request will be a POST request, with the body of the request being a JSON string of the parameters passed to the entrypoint tool. | ||
- For example, if the script is run like `gptscript script.gpt '{"first":"hello","second":"world"}'`, then the body of the request will be `{"first":"hello","second":"world"}`. | ||
- The path of the request will be `/myPath`. | ||
- The hostname is `my-daemon.daemon.gptscript.local`. When sending a request to a daemon tool, the hostname must always start with the daemon tool's name, followed by `.daemon.gptscript.local`. | ||
- GPTScript recognizes this hostname and determines the correct port number to send the request to, on localhost. | ||
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### Running the Example | ||
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Now let's try running it: | ||
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```bash | ||
gptscript script.gpt '{"first":"hello","second":"world"}' | ||
``` | ||
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``` | ||
OUTPUT: | ||
Body: {"first":"hello","second":"world"} | ||
Path: /myPath | ||
``` | ||
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This is exactly what we expected. This is a silly, small example just to demonstrate how this feature works. | ||
A real-world situation would involve several different tools sending different HTTP requests to the daemon tool, | ||
likely with an LLM determining when to call which tool. | ||
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## Real-World Example | ||
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To see a real-world example of a daemon tool, check out the [GPTScript Browser tool](https://github.com/gptscript-ai/browser). |
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