Tools is a powerful feature of LLM models
that allows you to augment the LLM reasoning with external tools.
These days, many LLM models come with a built-in support for tools. However, some of them
don’t… like OpenAI’s o1-preview and o1-mini.
Fallback tools
With GenAIScript 1.72.0, we introduce the concept of fallback tools.
Basically, it consists of a system script that “teaches” the LLM model about available tools and how to call them.
A tool example
Here is an example of a tool that generates a random number between 0 and 1.
o1-mini trace (using GitHub Models)
gemma2 model (using Ollama)
Activation
The fallback tool mode is automatically activated for known LLM models that don’t support tools natively. The list is not complete
so open an issue if you stumble upon a model that should have fallback tools enabled.
It can also be activated manually (see documentation).
GenAIScript defines an agent as a tool that
runs an inline prompt to accomplish a task. The agent LLM is typically augmented with
additional tools.
In this blog post, we’ll walk through building a user interaction agent that enables the agent to ask questions to the user.
Let’s dive into understanding how to create an “Agent that can ask questions to the user.”
You can find the full script on GitHub right here.
Metadata
The script is written in JavaScript. It starts by declaring the metadata to make the script available as a system script,
which can be reused in other scripts.
This line sets up the title for our system, making it clear that it’s intended to interact with the user by asking questions.
title and description
The defAgent function defines the behavior of our agent. It takes an agent identifier and a description. These two are quite important,
as they will help the “host” LLM choose to use this agent.
GenAIScript will automatically append a description of all the tools used by the agent prompt so you don’t have to worry about that part in the description.
prompt
The third argument is a string or a function to craft prompt instructions for the agent LLM call. The agent implementation already contains generic prompting
to make the prompt behave like an agent, but you can add more to specify a role, tone, and dos and don’ts.
model configuration
The last argument is a set of model options, similar to runPrompt, to configure the LLM call made by the agent.
In particular, this is where you list the tools that the agent can use.
How to use the agent
The agent is used like any other tool by referencing it in the script options.
Let’s try it!
Let’s try the agent with:
and let’s look at the results…
✔ What would be the most unexpected thing to find inside a refrigerator? toaster
✔ Based on your answer, which of the following would also be unexpected to find inside a refrigerator? A television
✔ Is your selection of ‘A television’ the correct unexpected item to find inside a refrigerator? yes
In this blog post, we’ll dive into a practical example showcasing how to leverage GenAIScript for automatic web page content analysis. GenAIScript uses the playwright browser automation library which allows to load, interact and inspect web pages.
Step-by-Step Explanation of the Code
The following snippet provides a concise and effective way to analyze a web page’s content using GenAIScript:
Let’s break down what each line of this script does:
1. Navigating to a Web Page
This line automatically navigates to the specified URL (https://example.com). The host.browse function is a powerful feature of GenAIScript that initializes a browser session and returns a page object for further interactions.
2. Taking a Screenshot
Here, the script captures a screenshot of the current view of the page. This is particularly useful for archiving or visual analysis.
3. Defining Images for Analysis
After capturing the screenshot, this line registers the image for further analysis. defImages is a function that makes the screenshot available to subsequent analytical or AI-driven functions in the script.
4. Extracting Text Content
This command extracts all text content from the page, which can be invaluable for content audits or textual analysis.
5. Storing Text for Further Use
The extracted text is then stored under the identifier PAGE_TEXT, allowing it to be referenced in later parts of the script or for documentation purposes.
6. Analyzing the Content
Finally, this line represents a call to an AI or script-defined function that analyzes the captured content and provides insights. This is where the real power of automation and AI integration into GenAIScript shines, enabling detailed analysis without manual intervention.
Conclusion
With a simple yet powerful script like the one discussed, GenAIScript makes it feasible to automate the process of web page content analysis. Whether you’re conducting competitive analysis, performing content audits, or simply archiving web pages, GenAIScript offers a scalable and efficient solution.
Agentic is a standard library of TypeScript AI tools optimized for both TS-usage as well as LLM-based usage, which is really important for testing and debugging.
Agentic brings support for a variety of online APIs, like Bing, Wolfram Alpha, Wikipedia, and more. You can register any Agentic tool in your script using defTool. Here’s an example of how to use the Weather tool:
In the world of open source, a well-maintained README file acts as the front door to your project. It’s often the first thing potential users and contributors see, and as such, it should be both informative and inviting. Today, we’re diving into the GenAIScript that helps keep the README of the GenAI project as fresh as a daisy! 🌼 Check out the actual script file for the details.
The script we’re analyzing is a maintenance tool designed to import relevant information from documentation and samples into the README to enhance its appeal to users. It ensures that the README is not just a static file but a vibrant, updated document that accurately reflects the features and capabilities of GenAI.
Line-by-Line Explanation
Let’s walk through the script code as if we are crafting it from the ground up:
Here, we’re defining the script’s metadata, including a description of its purpose and the tools it will utilize. The fs tool indicates file system operations will be involved.
These lines declare two important files: the README itself and a FEATURES file that contains information to be imported into the README.
In this template literal, we’re outlining the tasks for the script, including guidelines for updating the README with features, samples, and documentation links while preserving certain sections unchanged.
Finally, we specify that the output of this script will be an updated README.md file.
How to Run the Script
To execute this maintenance script, you’ll need the GenAIScript CLI. If you haven’t installed it yet, head over to the official documentation for installation instructions. Once you have the CLI ready, run the following command in your terminal:
This command will kick off the script and apply the enhancements to your README file, ensuring it’s up-to-date and user-friendly.
Conclusion
A meticulous README is a hallmark of a well-maintained open source project. With this GenAIScript, the GenAI project sets an excellent example of automating the upkeep of project documentation. Embrace the power of automation to keep your project’s welcome mat clean and welcoming. Happy coding! 👨💻👩💻
Ever wondered how to leverage the power of AI and Large Language Models (LLMs) in your projects? Look no further!
This post will introduce you to GenAIScript, a tool designed to simplify the creation of prompts and interactions with LLMs. Let’s dive in! 🌊
What is GenAIScript?
GenAIScript uses a stylized version of JavaScript to generate prompts, which are then sent to an LLM.
Scripts are stored as files (genaisrc/*.genai.mjs), executed to produce the prompt text and structured results (files, diagnostics) are extracted automatically.
Getting Started
Here’s a simple example to get you started. Create a file named poem.genai.mjs in the genaisrc folder and add the following code:
When executed, this script will generate the following prompt:
👤 User🤖 Assistant
Adding Context
GenAIScript can also use context variables, allowing you to interact with files or other data sources. Let’s see an example where we define a context variable using env.files:
Execute this script to see the generated user message and the assistant’s response. The context variable FILES will contain the list of files in the environment.
👤 User🤖 Assistant
Metadata and Script Configuration
You can add metadata to your script using the script function. This helps in organizing and configuring the script, including specifying the model and other parameters. GenAIScript supports various LLM providers, such as OpenAI, Azure OpenAI,
GitHub Models, Ollama and more.
Next Steps
Getting started guide to configure and start using GenAIScript.