genaiscript

Automatable GenAI Scripting

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GenAIScript is a scripting environment designed to facilitate file ingestion, prompt development, and structured data extraction. Users can define metadata and model configurations, specify data sources, and define tasks to extract specific information. The tool provides a convenient way to analyze files and extract desired content in a structured format. It offers a user-friendly interface for working with data and automating data extraction processes, making it suitable for various data processing tasks.

README:

GenAIScript

🚀 JavaScript-ish environment with convenient tooling for file ingestion, prompt development, and structured data extraction.

📄 Read the ONLINE DOCUMENTATION at microsoft.github.io/genaiscript
🎙️ Listen to the podcast (generated by NotebookLM).

https://github.com/user-attachments/assets/ce181cc0-47d5-41cd-bc03-f220407d4dd0

🌟 Introduction

GenAIScript is a powerful scripting environment tailored for building and managing Large Language Model (LLM) prompts with ease. Whether you are a developer, data scientist, or researcher, GenAIScript provides the tools you need to create, debug, and share scripts efficiently.

🤖 This readme is maintained by the readme-updater script.

🚀 Quickstart Guide

Get started quickly by installing the Visual Studio Code Extension or using the command line.

✨ Features

🎨 Stylized JavaScript & TypeScript

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, and Test your scripts in Visual Studio Code or with the command line.

🔗 Reuse and Share Scripts

Scripts are files! They can be versioned, shared, and forked.

// 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 using the ${schema} schema.`

📋 Data Schemas

Define, validate, and 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, ...

def("PDF", env.files, { endsWith: ".pdf" })
const { pages } = await parsers.PDF(env.files[0])

📊 Ingest Tables from CSV, XLSX, ...

Manipulate tabular data from CSV, XLSX, ...

def("DATA", env.files, { endsWith: ".csv", sliceHead: 100 })
const rows = await parsers.CSV(env.files[0])
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
The quick brown fox jumps over the lazy dog.

🔍 File Search

Grep or fuzz search files.

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

LLM Tools

defTool(
    "weahter",
    "query a weather web api",
    { location: "string" },
    async (args) =>
        await fetch(`https://weather.api.api/?location=${args.location}`)
)

LLM Agents

defAgent(
    "git",
    "Query a repository using Git to accomplish tasks.",
    `Your are a helpful LLM agent that can use the git tools to query the current repository.
    Answer the question in QUERY.
    - The current repository is the same as github repository.`,
    { model, system: ["system.github_info"], tools: ["git"] }
)

then use it as a tool

script({ tools: "agent_git" })

$`What is the current branch?`

🔍 RAG Built-in

Vector search.

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

🐙 GitHub Models and GitHub Copilot

Run models through 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" })

🐍 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.

for (const file of env.files) {
    const { text } = await runPrompt((_) => {
        _.def("FILE", file)
        _.$`Summarize the FILE.`
    })
    def("SUMMARY", text)
}
$`Summarize all the summaries.`

🅿️ Prompty support

Run your Prompty files as well!

---
name: poem
---

Write me a 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 Antarctica.",
}})

Contributing

We accept contributions! Checkout the CONTRIBUTING page for details and developer setup.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

For Tasks:

Click tags to check more tools for each tasks

extract titles analyze files automate data extraction define metadata develop prompts

For Jobs:

data analyst data scientist machine learning engineer research analyst software developer

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