Generative AI Scripting

Prompting is Coding

Programmatically assemble prompts for LLMs using JavaScript.

extract-data.genai.mjs

// define the context
def("FILE", env.files, { endsWith: ".pdf" })
// structure the data
const schema = defSchema("DATA", { type: "array", items: { type: "string" } })
// assign the task
$`Analyze FILE and extract data to JSON.`
// save results to file
defFileOutput("*.pdf.txt", "Extracted data", { schema })
// tools
defTool("weather", "live weahter", { city: "Paris" }, /* schema */
    async ({ city }) => { ... "sunny" }) /* callback */
...

Next steps

Install the extension

Install the Visual Studio Code Extension to get started.

Configure your LLMs

Configure the secrets to access your LLMs.

Write your first script

Follow Getting Started to write your first script.

Read the docs

Learn more about GenAIScript in the Scripting Reference.

Features

GenAIScript brings essential LLM prompt tooling into a cohesive scripting environment.

Stylized JavaScript

Build prompts programmatically using JavaScript or TypeScript.

def("FILE", env.files, { endsWith: ".pdf" })
$`Summarize FILE. Today is ${new Date()}.`

Fast Development Loop

Edit, Debug, Run, Test your scripts in Visual Studio Code or with a command line.

Reuse and Share Scripts

Scripts are files! They can be versioned, shared, forked, …

• Directorygenaisrc

  • my-script.genai.mjs
  • another-great-script.genai.mjs

Data Schemas

Define, validate, repair data using schemas.

const data = defSchema("MY_DATA",
    { type: "array", items: { ... }, })
$`Extract data from files using ${data} schema.`

Ingest text from PDFs, DOCX, ...

Manipulate PDFs, DOCX, …

// automatically convert to text
def("PDF", env.files, { endsWith: ".pdf" })
// or parse and process
const { pages } = await parsers.PDF(env.files[0])

Ingest tables from CSV, XLSX, ..

Manipulate tabular data from CSV, XLSX, …

// automatically convert to text
def("DATA", env.files, {
    endsWith: ".csv",
    // take top 100 rows
    sliceHead: 100,
})
// or parse to JavaScript object array
const rows = await parsers.CSV(env.files[0])
// render as markdown table
defData("ROWS", rows, { sliceHead: 100 })

Generate Files

Extract files and diff from the LLM output. Preview changes in Refactoring UI.

$`Save the result in poem.txt.`

FILE ./poem.txt
```txt
The quick brown fox jumps over the lazy dog.
```

• poem.txt extracted by genaiscript

File search

Grep or fuzz search files

const { files } = await workspace.grep(/[a-z][a-z0-9]+/, "**/*.md")

Browser automation

Browse and scrape the web with Playwright.

const page = await host.browse("https://...")
const table = await page.locator("table[...]").innerHTML()
def("TABLE", HTML.convertToMarkdown(table))

RAG built-in

Vector search.

const { files } = await retrieval.vectorSearch("cats", "**/*.md")

GitHub Models and GitHub Copilot

Run models through GitHub using GitHub Models or GitHub Copilot.

script({ ..., model: "github:gpt-4o" })

Local Models

Run your scripts with Open Source models, like Phi-3, using Ollama, LocalAI…

script({ ..., model: "ollama:phi3" })

LLM Tools

Register JavaScript functions as LLM tools

defTool("weather", "live weahter",
    { city: "Paris" }, // schema
    async ({ city }) => // callback
        { ... "sunny" }
)

or use built-in @agentic tools

import { WeatherClient } from "@agentic/weather"
defTool(new WeatherClient())

Code Interpreter

Let the LLM run code in a sandboxed execution environment.

script({ tools: ["python_code_interpreter"] })

Containers

Run code in Docker containers.

const c = await host.container({
    image: "python:alpine",
})
const res = await c.exec("python", ["--version"])

LLM Composition

Run LLMs to build your LLM prompts.

// summarize each files individually
for (const file of env.files) {
    const { text } = await runPrompt((_) => {
        _.def("FILE", file)
        _.$`Summarize the FILE.`
    })
    // use result in main prompt
    _.def("SUMMARY", text)
}
// use summary
$`Summarize all the summaries.`

Prompty

Run or convert Prompty files using GenAIScript.

poem.prompty

---
name: poem
---

Write a short poem.

Automate with CLI

Automate using the CLI, integrate reports in your CI/CD pipeline.

npx genaiscript run tlaplus-linter "*.tla"

Pull Request Reviews

Integrate into your Pull Requests checks through comments, reviews or description updates. Supports GitHub Actions and Azure DevOps pipelines.

npx genaiscript ... --pull-request-reviews

Tests and Evals

Build reliable prompts using tests and evals powered by promptfoo.

script({ ..., tests: {
  files: "penguins.csv",
  rubric: "is a data analysis report",
  facts: "The data refers about penguin population in Antartica.",
}})