GenAIスクリプト

$`Write a 'hello world' poem.`

// read files
const file = await workspace.readText("data.txt")
// include the file content in the prompt in a context-friendly way
def("DATA", file)
// the task
$`Analyze DATA and extract data in JSON in data.json.`

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

// 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.`

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

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

def("DATA", env.files, { endsWith: ".csv", sliceHead: 100 })
const rows = await parsers.CSV(env.files[0])
defData("ROWS", rows, { sliceHead: 100 })

$`Save the result in poem.txt.`

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

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

const joke = await classify(
 "Why did the chicken cross the road? To fry in the sun.",
 {
 yes: "funny",
 no: "not funny",
 }
)

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

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"] }
)

script({ tools: "agent_git" })

$`Do a statistical analysis of the last commits`

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

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

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

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

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

// transcribe
const transcript = await transcript("path/to/audio.mp3")
// screenshots at segments
const frames = await ffmpeg.extractFrames("path_url_to_video", { transcript })
def("TRANSCRIPT", transcript)
def("FRAMES", frames)

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

---
name: poem
---

Write me a poem

{
 "secretPatterns": {
 ...,
 "OpenAI API Key": "sk-[A-Za-z0-9]{32,48}"
 }
}

npx genaiscript run tlaplus-linter "*.tla"

import { run } from "genaiscript/api"

const res = await run("tlaplus-linter", "*.tla")

script({ ...,
 system: ["system.safety_harmful_content", ...],
 contentSafety: "azure" // use azure content safety
})

const safety = await host.contentSafety()
const res = await safety.detectPromptInjection(env.vars.input)

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

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

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