生成式AI脚本编写

$`Summarize ${env.files}. Today is ${new Date()}.`

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

defTool({
    memory: {
        command: "npx",
        args: ["-y", "@modelcontextprotocol/server-memory"],
    },
})

script({
    parameters: {
        question: "What is the weather in Paris?",
    },
})
$`Answer the question ${env.parameters.question}.`

defAgent(
    "git",
    "Agent that answer git questions for the current repo",
    "You are a helpful expert in using git.",
    { tools: ["git"] }
)

script({ tools: "agent" })

$`Do a statistical analysis of the last commits`

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

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

// 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 })

const transcript = await transcript("path/to/audio.mp3")
const { srt, vtt, segments } = transcript

defImages(images, { autoCrop: true, details: "low" })

const frames = await ffmpeg.extractFrames("...", { count: 10 })
defImages(frames, { details: "low" })

$`Save the result in poem.txt.`

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

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

const pages = await retrieval.webSearch("what are the latest news about AI?")

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

const index = await retrieval.index("animals", { type: "azure_ai_search" })
await index.insertOrUpdate(env.files)
const docs = await index.search("cat dog")

script({ ...,
    systemSafety: "default",
    contentSafety: "azure" // use azure content safety
})

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

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

script({ ..., model: "azure_ai_inference:o3-mini"})

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

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

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

// 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.`

const { image, revisedPrompt } = await generateImage(
    `a cute cat. only one. photographic, high details. 4k resolution.`
)

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

importTemplate("poem.prompty", { something: "code " })

npx genaiscript run tlaplus-linter "*.tla"

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

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