windmill/system_prompts/auto-generated/script.md

# Windmill Script Writing Guide

## General Principles

- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters
- Libraries are installed automatically - do not show installation instructions
- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments
- Where the language has a Windmill client (`wmill`), use it to interact with the platform

## Return Values

- A script can return any JSON-serializable value; a SQL script returns the rows its query produces
- Return values become available to subsequent flow steps via `results.step_id`

## Preprocessor Scripts

Preprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.

A preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).

The returned object determines the parameter values passed to the flow.
e.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.


# Ansible

Windmill runs Ansible playbooks with `ansible-playbook`. A script is a single YAML
document made of two parts separated by a `---` line: a Windmill **header** and one or
more standard Ansible **plays**.

## Structure

```yaml
---
# Windmill header: configures inventories, file resources, arguments and dependencies
extra_vars:
  world_qualifier:
    type: string
dependencies:
  galaxy:
    collections:
      - name: community.general
  python:
    - jmespath
---
# Standard Ansible plays
- name: Echo
  hosts: 127.0.0.1
  connection: local
  tasks:
    - name: Print debug message
      debug:
        msg: "Hello, {{ world_qualifier }} world!"
```

## Header

The header is **not** standard Ansible — it is parsed by Windmill to build the script's
inputs and runtime environment. Supported keys:

- `extra_vars`: defines the script arguments. Each entry is passed to the playbook via
  `--extra-vars` and becomes a Jinja variable usable as `{{ name }}` in the plays. Give
  each argument a `type` (`string`, `number`, `boolean`, `object`, ...) so Windmill can
  generate the input form.
- `inventory`: lists inventories. Use `resource_type: ansible_inventory` (optionally
  pinned with `resource: u/user/your_resource`) or `resource_type: dynamic_inventory`.
- `files`: writes Windmill resources/variables to files before the run, e.g.
  `- resource: u/user/template` with `target: ./config.j2`, or
  `- variable: u/user/ssh_key` with `target: ./ssh_key` and `mode: '0600'`.
- `dependencies`: `galaxy` collections/roles (installed with `ansible-galaxy`) and
  `python` pip packages available to the playbook.
- `options`: extra `ansible-playbook` flags such as `- verbosity: vvv`.
- `vault_password`: a Windmill variable path to use as the Ansible Vault password.

## Arguments

Reference header `extra_vars` directly as Jinja variables in the plays:

```yaml
extra_vars:
  name:
    type: string
  count:
    type: number
---
- hosts: localhost
  tasks:
    - debug:
        msg: "{{ name }} x {{ count }}"
```

## Environment variables

Windmill contextual variables are available as environment variables and read with the
`env` lookup:

```yaml
- debug:
    msg: "Running in workspace {{ lookup('env', 'WM_WORKSPACE') }}"
```

## Output

To return a result, write JSON to a `result.json` file in the job directory:

```yaml
- hosts: localhost
  tasks:
    - name: Write result
      copy:
        content: "{{ { 'ok': true, 'value': 42 } | to_json }}"
        dest: result.json
```


# Bash

## Structure

Do not include `#!/bin/bash`. Arguments are obtained as positional parameters:

```bash
# Get arguments
var1="$1"
var2="$2"

echo "Processing $var1 and $var2"

# Return JSON by echoing to stdout
echo "{\"result\": \"$var1\", \"count\": $var2}"
```

**Important:**
- Do not include shebang (`#!/bin/bash`)
- Arguments are always strings
- Access with `$1`, `$2`, etc.

## Output

The script output is captured as the result. For structured data, output valid JSON:

```bash
name="$1"
count="$2"

# Output JSON result
cat << EOF
{
  "name": "$name",
  "count": $count,
  "timestamp": "$(date -Iseconds)"
}
EOF
```

## Environment Variables

Environment variables set in Windmill are available:

```bash
# Access environment variable
echo "Workspace: $WM_WORKSPACE"
echo "Job ID: $WM_JOB_ID"
```


# BigQuery

Arguments use `@name` syntax.

Name the parameters by adding comments before the statement:

```sql
-- @name1 (string)
-- @name2 (int64) = 0
SELECT * FROM users WHERE name = @name1 AND age > @name2;
```

## Receiving an S3Object as a script parameter

Declare the arg with type `(s3object)`. Windmill renders an S3 file picker for
it, downloads the file, and binds it as a `STRING` JSON parameter — Parquet/CSV
files are decoded server-side into a JSON array of records, JSON/JSONL pass
through. Consume with `JSON_EXTRACT_ARRAY` / `JSON_VALUE`:

```sql
-- @file (s3object)
SELECT
  CAST(JSON_VALUE(row, '$.id') AS INT64) AS id,
  JSON_VALUE(row, '$.name') AS name
FROM UNNEST(JSON_EXTRACT_ARRAY(@file)) AS row;
```

## Streaming query results to S3

Add a `-- s3` directive at the top of the script to stream the result set to S3
instead of returning rows. Windmill writes the file and returns its `S3Object`
as the script result.

```sql
-- s3 prefix=exports/users format=parquet
SELECT id, name FROM users;
```

All keys are optional: `prefix` (object key prefix), `storage` (named storage —
omit to use the workspace default), `format` (`json` (default), `parquet`, or
`csv`). Use this for large result sets — rows stream directly to S3 instead of
being buffered, bypassing the 10000-row return cap.


# TypeScript (Bun)

Bun runtime with full npm ecosystem and fastest execution. **Bun is the default and preferred TypeScript runtime** — choose it for any TypeScript script unless there is a major reason to use Deno for that specific use-case.

## Structure

Export a single **async** function called `main`:

```typescript
export async function main(param1: string, param2: number) {
  // Your code here
  return { result: param1, count: param2 };
}
```

Do not call the main function. Libraries are installed automatically.

## Resource Types

On Windmill, credentials and configuration are stored in resources and passed as parameters to main.

Use the `RT` namespace for resource types:

```typescript
export async function main(stripe: RT.Stripe) {
  // stripe contains API key and config from the resource
}
```

Only use resource types if you need them to satisfy the instructions. Always use the RT namespace.

## Imports

```typescript
import Stripe from "stripe";
import { someFunction } from "some-package";
```

## Prefer `//native` when the runtime allows it

If a script only needs `fetch` and the JavaScript standard library — including when it uses `windmill-client` — prefer making it a **native** script: add `//native` as the first line and write it with the `write-script-bunnative` skill. Native scripts run on a lightweight V8 isolate, start faster, and parallelize heavily. `windmill-client` works on the native worker (its calls go over `fetch`), so needing the Windmill client is **not** a reason to avoid `//native`. Use the regular `bun` language only when the code (or a dependency) needs Node/Bun runtime APIs — `node:*` modules, the filesystem, child processes, or native addons.

## Windmill Client

Import the windmill client for platform interactions:

```typescript
import * as wmill from "windmill-client";
```

**Prefer `windmill-client` over raw `fetch` for anything that talks to Windmill** — reading resources/variables/states, running scripts and flows, S3 object operations, etc. It handles auth, the workspace, and the base URL for you, so you don't hand-roll URLs or tokens. Reserve `fetch` for calling *external* HTTP APIs that aren't Windmill.

The full `windmill-client` API reference (every exported function and its signature) is included below — consult it for the exact method to use instead of guessing or falling back to `fetch`.

## Preprocessor Scripts

For preprocessor scripts, the function should be named `preprocessor` and receives an `event` parameter:

```typescript
type Event = {
  kind:
    | "webhook"
    | "http"
    | "websocket"
    | "kafka"
    | "email"
    | "nats"
    | "postgres"
    | "sqs"
    | "mqtt"
    | "amqp"
    | "gcp"
    | "azure";
  body: any;
  headers: Record<string, string>;
  query: Record<string, string>;
};

export async function preprocessor(event: Event) {
  return {
    param1: event.body.field1,
    param2: event.query.id,
  };
}
```

## S3 Object Operations

Windmill provides built-in support for S3-compatible storage operations. The `wmill.S3Object` type covers both the `s3://storage/key` URI form (`s3:///key` for the workspace default storage) and the `{ s3, storage? }` record form — always use it instead of redefining your own.

### Receiving an S3Object as a script parameter

```typescript
import * as wmill from "windmill-client";

export async function main(file: wmill.S3Object) {
  const content = await wmill.loadS3File(file);
  // ...
}
```

### S3 operations

```typescript
import * as wmill from "windmill-client";

// Load file content from S3
const content: Uint8Array = await wmill.loadS3File(s3object);

// Load file as stream
const blob: Blob = await wmill.loadS3FileStream(s3object);

// Write file to S3
const result: wmill.S3Object = await wmill.writeS3File(
  s3object, // Target path (or undefined to auto-generate)
  fileContent, // string or Blob
  s3ResourcePath // Optional: specific S3 resource to use
);
```


# TypeScript (Bun Native)

Native TypeScript execution. Native scripts are Bun scripts that run on the native worker — a lightweight V8 isolate that exposes `fetch` and the JavaScript standard library — and can be heavily parallelized. Every script MUST start with `//native` on its first line so Windmill routes it to the native worker; without it the exact same script runs on the regular Bun worker. You may import npm packages and other Windmill scripts (e.g. `./helper.ts`) — imports are resolved and bundled just like a regular Bun script — as long as everything (your code and its dependencies) relies only on `fetch` and the standard library. Libraries that need Node/Bun runtime APIs (filesystem, `node:*` modules, child processes, native addons) will not work on the native worker; use the regular `bun` language for those.

## Structure

Export a single **async** function called `main`:

```typescript
//native
export async function main(param1: string, param2: number) {
  // Your code here
  return { result: param1, count: param2 };
}
```

Do not call the main function.

## Resource Types

On Windmill, credentials and configuration are stored in resources and passed as parameters to main.

Use the `RT` namespace for resource types:

```typescript
//native
export async function main(stripe: RT.Stripe) {
  // stripe contains API key and config from the resource
}
```

Only use resource types if you need them to satisfy the instructions. Always use the RT namespace.

## Imports

**The constraint is the runtime, not the import list.** You may import npm packages and relative Windmill scripts; they are resolved and bundled exactly like a regular Bun script. But the native worker only provides `fetch` and the JavaScript standard library, so any imported code must work using only those. Anything requiring Node/Bun built-ins (`node:fs`, `child_process`, the `Bun` API, native modules) belongs in a regular `bun` script instead. Use the globally available `fetch` for HTTP:

```typescript
//native
export async function main(url: string) {
  const response = await fetch(url);
  return await response.json();
}
```

## Windmill Client

`windmill-client` works on the native worker (its calls go over `fetch`), so use it as the **preferred way to talk to Windmill** — reading resources/variables/states, running scripts and flows, and the S3 helpers below (`loadS3File`, `loadS3FileStream`, `writeS3File`, `S3Object`). It handles auth, the workspace, and the base URL for you. Reserve raw `fetch` for calling *external* HTTP APIs that aren't Windmill.

The full `windmill-client` API reference (every exported function and its signature) is included below — consult it for the exact method instead of hand-rolling a `fetch` against the Windmill API.

## Preprocessor Scripts

For preprocessor scripts, the function should be named `preprocessor` and receives an `event` parameter:

```typescript
//native
type Event = {
  kind:
    | "webhook"
    | "http"
    | "websocket"
    | "kafka"
    | "email"
    | "nats"
    | "postgres"
    | "sqs"
    | "mqtt"
    | "amqp"
    | "gcp"
    | "azure";
  body: any;
  headers: Record<string, string>;
  query: Record<string, string>;
};

export async function preprocessor(event: Event) {
  return {
    param1: event.body.field1,
    param2: event.query.id,
  };
}
```

## S3 Object Operations

Windmill provides built-in support for S3-compatible storage operations. The `wmill.S3Object` type covers both the `s3://storage/key` URI form (`s3:///key` for the workspace default storage) and the `{ s3, storage? }` record form — always use it instead of redefining your own.

### Receiving an S3Object as a script parameter

```typescript
//native
import * as wmill from "windmill-client";

export async function main(file: wmill.S3Object) {
  const content = await wmill.loadS3File(file);
  // ...
}
```

### S3 operations

```typescript
//native
import * as wmill from "windmill-client";

// Load file content from S3
const content: Uint8Array = await wmill.loadS3File(s3object);

// Load file as stream
const blob: Blob = await wmill.loadS3FileStream(s3object);

// Write file to S3
const result: wmill.S3Object = await wmill.writeS3File(
  s3object, // Target path (or undefined to auto-generate)
  fileContent, // string or Blob
  s3ResourcePath // Optional: specific S3 resource to use
);
```


# C#

The script must contain a public static `Main` method inside a class:

```csharp
public class Script
{
    public static object Main(string name, int count)
    {
        return new { Name = name, Count = count };
    }
}
```

**Important:**
- Class name is irrelevant
- Method must be `public static`
- Return type can be `object` or specific type

## NuGet Packages

Add packages using the `#r` directive at the top:

```csharp
#r "nuget: Newtonsoft.Json, 13.0.3"
#r "nuget: RestSharp, 110.2.0"

using Newtonsoft.Json;
using RestSharp;

public class Script
{
    public static object Main(string url)
    {
        var client = new RestClient(url);
        var request = new RestRequest();
        var response = client.Get(request);
        return JsonConvert.DeserializeObject(response.Content);
    }
}
```


# TypeScript (Deno)

Deno runtime with npm support via `npm:` prefix and native Deno libraries.

**Prefer Bun (`write-script-bun`) for TypeScript.** Only use Deno when the script specifically requires the Deno runtime — Deno's standard library or `deno.land` URL imports that have no npm equivalent. For all other TypeScript, use Bun instead.

## Structure

Export a single **async** function called `main`:

```typescript
export async function main(param1: string, param2: number) {
  // Your code here
  return { result: param1, count: param2 };
}
```

Do not call the main function. Libraries are installed automatically.

## Resource Types

On Windmill, credentials and configuration are stored in resources and passed as parameters to main.

Use the `RT` namespace for resource types:

```typescript
export async function main(stripe: RT.Stripe) {
  // stripe contains API key and config from the resource
}
```

Only use resource types if you need them to satisfy the instructions. Always use the RT namespace.

## Imports

```typescript
// npm packages use npm: prefix
import Stripe from "npm:stripe";
import { someFunction } from "npm:some-package";

// Deno standard library
import { serve } from "https://deno.land/std/http/server.ts";
```

## Windmill Client

Import the windmill client for platform interactions:

```typescript
import * as wmill from "windmill-client";
```

**Prefer `windmill-client` over raw `fetch` for anything that talks to Windmill** — reading resources/variables/states, running scripts and flows, S3 object operations, etc. It handles auth, the workspace, and the base URL for you. Reserve `fetch` for calling *external* HTTP APIs that aren't Windmill.

The full `windmill-client` API reference (every exported function and its signature) is included below — consult it for the exact method instead of guessing or falling back to `fetch`.

## Preprocessor Scripts

For preprocessor scripts, the function should be named `preprocessor` and receives an `event` parameter:

```typescript
type Event = {
  kind:
    | "webhook"
    | "http"
    | "websocket"
    | "kafka"
    | "email"
    | "nats"
    | "postgres"
    | "sqs"
    | "mqtt"
    | "amqp"
    | "gcp"
    | "azure";
  body: any;
  headers: Record<string, string>;
  query: Record<string, string>;
};

export async function preprocessor(event: Event) {
  return {
    param1: event.body.field1,
    param2: event.query.id,
  };
}
```

## S3 Object Operations

Windmill provides built-in support for S3-compatible storage operations. The `wmill.S3Object` type covers both the `s3://storage/key` URI form (`s3:///key` for the workspace default storage) and the `{ s3, storage? }` record form — always use it instead of redefining your own.

### Receiving an S3Object as a script parameter

```typescript
import * as wmill from "windmill-client";

export async function main(file: wmill.S3Object) {
  const content = await wmill.loadS3File(file);
  // ...
}
```

### S3 operations

```typescript
import * as wmill from "windmill-client";

// Load file content from S3
const content: Uint8Array = await wmill.loadS3File(s3object);

// Load file as stream
const blob: Blob = await wmill.loadS3FileStream(s3object);

// Write file to S3
const result: wmill.S3Object = await wmill.writeS3File(
  s3object, // Target path (or undefined to auto-generate)
  fileContent, // string or Blob
  s3ResourcePath // Optional: specific S3 resource to use
);
```


# DuckDB

Arguments are defined with comments and used with `$name` syntax:

```sql
-- $name (text) = default
-- $age (integer)
SELECT * FROM users WHERE name = $name AND age > $age;
```

## Ducklake Integration

Attach Ducklake for data lake operations:

```sql
-- Main ducklake
ATTACH 'ducklake' AS dl;

-- Named ducklake
ATTACH 'ducklake://my_lake' AS dl;

-- Then query
SELECT * FROM dl.schema.table;
```

## External Database Connections

Connect to external databases using resources:

```sql
ATTACH '$res:path/to/resource' AS db (TYPE postgres);
SELECT * FROM db.schema.table;
```

## S3 File Operations

Read files from S3 storage:

```sql
-- Default storage
SELECT * FROM read_csv('s3:///path/to/file.csv');

-- Named storage
SELECT * FROM read_csv('s3://storage_name/path/to/file.csv');

-- Parquet files
SELECT * FROM read_parquet('s3:///path/to/file.parquet');

-- JSON files
SELECT * FROM read_json('s3:///path/to/file.json');
```

### Receiving an S3Object as a script parameter

Declare the arg with type `(s3object)`. Windmill renders an S3 file picker for it
and binds the arg as the bare `s3://storage/key` URI, which DuckDB's reader
functions consume directly:

```sql
-- $file (s3object)
SELECT * FROM read_parquet($file);
```

Works with any DuckDB reader: `read_csv($file)`, `read_json($file)`, etc.

### Writing query results to S3

DuckDB writes to S3 natively via `COPY ... TO`:

```sql
COPY (SELECT * FROM users) TO 's3:///exports/users.parquet' (FORMAT PARQUET);
```

Use this instead of the `-- s3` streaming directive supported by the other SQL
dialects — that directive is not available in DuckDB.


# Go

## Structure

The file package must be `inner` and export a function called `main`:

```go
package inner

func main(param1 string, param2 int) (map[string]interface{}, error) {
    return map[string]interface{}{
        "result": param1,
        "count":  param2,
    }, nil
}
```

**Important:**
- Package must be `inner`
- Return type must be `({return_type}, error)`
- Function name is `main` (lowercase)

## Return Types

The return type can be any Go type that can be serialized to JSON:

```go
package inner

type Result struct {
    Name  string `json:"name"`
    Count int    `json:"count"`
}

func main(name string, count int) (Result, error) {
    return Result{
        Name:  name,
        Count: count,
    }, nil
}
```

## Error Handling

Return errors as the second return value:

```go
package inner

import "errors"

func main(value int) (string, error) {
    if value < 0 {
        return "", errors.New("value must be positive")
    }
    return "success", nil
}
```


# GraphQL

## Structure

Write GraphQL queries or mutations. Arguments can be added as query parameters:

```graphql
query GetUser($id: ID!) {
  user(id: $id) {
    id
    name
    email
  }
}
```

## Variables

Variables are passed as script arguments and automatically bound to the query:

```graphql
query SearchProducts($query: String!, $limit: Int = 10) {
  products(search: $query, first: $limit) {
    edges {
      node {
        id
        name
        price
      }
    }
  }
}
```

## Mutations

```graphql
mutation CreateUser($input: CreateUserInput!) {
  createUser(input: $input) {
    id
    name
    createdAt
  }
}
```


# Java

The script must contain a Main public class with a `public static main()` method:

```java
public class Main {
    public static Object main(String name, int count) {
        java.util.Map<String, Object> result = new java.util.HashMap<>();
        result.put("name", name);
        result.put("count", count);
        return result;
    }
}
```

**Important:**
- Class must be named `Main`
- Method must be `public static Object main(...)`
- Return type is `Object` or `void`

## Maven Dependencies

Add dependencies using comments at the top:

```java
//requirements:
//com.google.code.gson:gson:2.10.1
//org.apache.httpcomponents:httpclient:4.5.14

import com.google.gson.Gson;

public class Main {
    public static Object main(String input) {
        Gson gson = new Gson();
        return gson.fromJson(input, Object.class);
    }
}
```


# Microsoft SQL Server (MSSQL)

Arguments use `@P1`, `@P2`, etc.

Name the parameters by adding comments before the statement:

```sql
-- @P1 name1 (varchar)
-- @P2 name2 (int) = 0
SELECT * FROM users WHERE name = @P1 AND age > @P2;
```

## Receiving an S3Object as a script parameter

Declare the arg with type `(s3object)`. Windmill renders an S3 file picker for
it, downloads the file, and binds it as `nvarchar(max)` JSON text — Parquet/CSV
files are decoded server-side into a JSON array of records, JSON/JSONL pass
through. Consume with `OPENJSON`:

```sql
-- @P1 file (s3object)
SELECT id, name
FROM OPENJSON(@P1)
WITH (id INT, name NVARCHAR(200));
```

## Streaming query results to S3

Add a `-- s3` directive at the top of the script to stream the result set to S3
instead of returning rows. Windmill writes the file and returns its `S3Object`
as the script result.

```sql
-- s3 prefix=exports/users format=parquet
SELECT id, name FROM users;
```

All keys are optional: `prefix` (object key prefix), `storage` (named storage —
omit to use the workspace default), `format` (`json` (default), `parquet`, or
`csv`). Use this for large result sets — rows stream directly to S3 instead of
being buffered as the script return value.


# MySQL

Arguments use `?` placeholders.

Name the parameters by adding comments before the statement:

```sql
-- ? name1 (text)
-- ? name2 (int) = 0
SELECT * FROM users WHERE name = ? AND age > ?;
```

## Receiving an S3Object as a script parameter

Declare the arg with type `(s3object)`. Windmill renders an S3 file picker for
it, downloads the file, and binds it as JSON text — Parquet/CSV files are
decoded server-side into a JSON array of records, JSON/JSONL pass through.
Consume with `JSON_TABLE`:

```sql
-- ? file (s3object)
SELECT id, name
FROM JSON_TABLE(?, '$[*]'
  COLUMNS (id INT PATH '$.id', name VARCHAR(200) PATH '$.name')
) AS r;
```

## Streaming query results to S3

Add a `-- s3` directive at the top of the script to stream the result set to S3
instead of returning rows. Windmill writes the file and returns its `S3Object`
as the script result.

```sql
-- s3 prefix=exports/users format=parquet
SELECT id, name FROM users;
```

All keys are optional: `prefix` (object key prefix), `storage` (named storage —
omit to use the workspace default), `format` (`json` (default), `parquet`, or
`csv`). Use this for large result sets — rows stream directly to S3 instead of
being buffered as the script return value.


# PHP

## Structure

The script must start with `<?php` and contain at least one function called `main`:

```php
<?php

function main(string $param1, int $param2) {
    return ["result" => $param1, "count" => $param2];
}
```

## Resource Types

On Windmill, credentials and configuration are stored in resources and passed as parameters to main.

You need to **redefine** the type of the resources that are needed before the main function. Always check if the class already exists using `class_exists`:

```php
<?php

if (!class_exists('Postgresql')) {
    class Postgresql {
        public string $host;
        public int $port;
        public string $user;
        public string $password;
        public string $dbname;
    }
}

function main(Postgresql $db) {
    // $db contains the database connection details
}
```

The resource type name has to be exactly as specified.

## Library Dependencies

Specify library dependencies as comments before the main function:

```php
<?php

// require:
// guzzlehttp/guzzle
// stripe/stripe-php@^10.0

function main() {
    // Libraries are available
}
```

One dependency per line. No need to require autoload, it is already done.


# PostgreSQL

Arguments are obtained directly in the statement with `$1::{type}`, `$2::{type}`, etc.

Name the parameters by adding comments at the beginning of the script (without specifying the type):

```sql
-- $1 name1
-- $2 name2 = default_value
SELECT * FROM users WHERE name = $1::TEXT AND age > $2::INT;
```

## Receiving an S3Object as a script parameter

Declare the arg with type `(s3object)`. Windmill renders an S3 file picker for
it, downloads the file, and binds it as a `jsonb` parameter — Parquet/CSV files
are decoded server-side into a JSON array of records, JSON/JSONL pass through.
Consume with `jsonb_to_recordset` (or any `jsonb` API):

```sql
-- $1 file (s3object)
SELECT *
FROM jsonb_to_recordset($1::jsonb) AS r(id INT, name TEXT);
```

## Streaming query results to S3

Add a `-- s3` directive at the top of the script to stream the result set to S3
instead of returning rows. Windmill writes the file and returns its `S3Object`
as the script result.

```sql
-- s3 prefix=exports/users format=parquet
SELECT id, name FROM users;
```

All keys are optional: `prefix` (object key prefix), `storage` (named storage —
omit to use the workspace default), `format` (`json` (default), `parquet`, or
`csv`). Use this for large result sets — rows stream directly to S3 instead of
being buffered as the script return value.


# PowerShell

## Structure

Arguments are obtained by calling the `param` function on the first line:

```powershell
param($Name, $Count = 0, [int]$Age)

# Your code here
Write-Output "Processing $Name, count: $Count, age: $Age"

# Return object
@{
    name = $Name
    count = $Count
    age = $Age
}
```

## Parameter Types

You can specify types for parameters:

```powershell
param(
    [string]$Name,
    [int]$Count = 0,
    [bool]$Enabled = $true,
    [array]$Items
)

@{
    name = $Name
    count = $Count
    enabled = $Enabled
    items = $Items
}
```

## Return Values

Return values by outputting them at the end of the script:

```powershell
param($Input)

$result = @{
    processed = $true
    data = $Input
    timestamp = Get-Date -Format "o"
}

$result
```


# Python

## Structure

The script must contain at least one function called `main`:

```python
def main(param1: str, param2: int):
    # Your code here
    return {"result": param1, "count": param2}
```

Do not call the main function. Libraries are installed automatically.

## Resource Types

On Windmill, credentials and configuration are stored in resources and passed as parameters to main.

You need to **redefine** the type of the resources that are needed before the main function as TypedDict:

```python
from typing import TypedDict

class postgresql(TypedDict):
    host: str
    port: int
    user: str
    password: str
    dbname: str

def main(db: postgresql):
    # db contains the database connection details
    pass
```

**Important rules:**

- The resource type name must be **IN LOWERCASE**
- Only include resource types if they are actually needed
- If an import conflicts with a resource type name, **rename the imported object, not the type name**
- Make sure to import TypedDict from typing **if you're using it**

## Imports

Libraries are installed automatically. Do not show installation instructions.

```python
import requests
import pandas as pd
from datetime import datetime
```

If an import name conflicts with a resource type:

```python
# Wrong - don't rename the type
import stripe as stripe_lib
class stripe_type(TypedDict): ...

# Correct - rename the import
import stripe as stripe_sdk
class stripe(TypedDict):
    api_key: str
```

## Windmill Client

Import the windmill client for platform interactions:

```python
import wmill
```

See the SDK documentation for available methods.

## Preprocessor Scripts

For preprocessor scripts, the function should be named `preprocessor` and receives an `event` parameter:

```python
from typing import TypedDict, Literal, Any

class Event(TypedDict):
    kind: Literal["webhook", "http", "websocket", "kafka", "email", "nats", "postgres", "sqs", "mqtt", "amqp", "gcp", "azure"]
    body: Any
    headers: dict[str, str]
    query: dict[str, str]

def preprocessor(event: Event):
    # Transform the event into flow input parameters
    return {
        "param1": event["body"]["field1"],
        "param2": event["query"]["id"]
    }
```

## S3 Object Operations

Windmill provides built-in support for S3-compatible storage operations.

### Receiving an S3Object as a script parameter

To accept a file from S3 as input to a script, type the parameter with `S3Object` (imported from `wmill`):

```python
import wmill
from wmill import S3Object

def main(file: S3Object):
    content = wmill.load_s3_file(file)
    # ...
```

### S3 operations

```python
import wmill

# Load file content from S3
content: bytes = wmill.load_s3_file(s3object)

# Load file as stream reader
reader: BufferedReader = wmill.load_s3_file_reader(s3object)

# Write file to S3
result: S3Object = wmill.write_s3_file(
    s3object,           # Target path (or None to auto-generate)
    file_content,       # bytes or BufferedReader
    s3_resource_path,   # Optional: specific S3 resource
    content_type,       # Optional: MIME type
    content_disposition # Optional: Content-Disposition header
)
```


# R

## Structure

Define a `main` function using `<-` or `=` assignment. Parameters become the script inputs:

```r
library(dplyr)
library(jsonlite)

main <- function(x, name = "default", flag = TRUE) {
    df <- tibble(x = x, name = name)
    result <- df %>% mutate(greeting = paste("Hello", name))
    return(toJSON(result, auto_unbox = TRUE))
}
```

**Important:**
- The `main` function is required
- Use `library()` to load packages — they are resolved and installed automatically
- `jsonlite` is always available (used internally for argument parsing)
- Return values must be JSON-serializable

## Parameters

R types map to Windmill types:
- `numeric` → float/int
- `character` → string
- `logical` → bool (use `TRUE`/`FALSE`)
- `list` → object/dict
- `NULL` → null

Default values are inferred from the function signature:

```r
main <- function(
    name,              # required string
    count = 10,        # optional int, default 10
    verbose = FALSE    # optional bool, default FALSE
) {
    # ...
}
```

## Resources and Variables

Use the built-in Windmill helpers (no import needed):

```r
main <- function() {
    # Get a variable
    api_key <- get_variable("f/my_folder/api_key")

    # Get a resource (returns a list)
    db <- get_resource("f/my_folder/postgres_config")
    host <- db$host
    port <- db$port

    return(list(host = host, port = port))
}
```

## Output

Return any JSON-serializable value from `main`. The return value becomes the step result:

```r
main <- function(x) {
    # Return a scalar
    return(x + 1)

    # Or a list (becomes JSON object)
    return(list(result = x + 1, status = "ok"))
}
```

## Annotations

Control execution behavior with comment annotations:

```r
#renv_verbose = true        # Show verbose renv output during resolution
#renv_install_verbose = true # Show verbose output during package installation
#sandbox = true              # Run in nsjail sandbox (requires nsjail)
```


# Rust

## Structure

The script must contain a function called `main` with proper return type:

```rust
use anyhow::anyhow;
use serde::Serialize;

#[derive(Serialize, Debug)]
struct ReturnType {
    result: String,
    count: i32,
}

fn main(param1: String, param2: i32) -> anyhow::Result<ReturnType> {
    Ok(ReturnType {
        result: param1,
        count: param2,
    })
}
```

**Important:**
- Arguments should be owned types
- Return type must be serializable (`#[derive(Serialize)]`)
- Return type is `anyhow::Result<T>`

## Dependencies

Packages must be specified with a partial cargo.toml at the beginning of the script:

```rust
//! ```cargo
//! [dependencies]
//! anyhow = "1.0.86"
//! reqwest = { version = "0.11", features = ["json"] }
//! tokio = { version = "1", features = ["full"] }
//! ```

use anyhow::anyhow;
// ... rest of the code
```

**Note:** Serde is already included, no need to add it again.

## Async Functions

If you need to handle async functions (e.g., using tokio), keep the main function sync and create the runtime inside:

```rust
//! ```cargo
//! [dependencies]
//! anyhow = "1.0.86"
//! tokio = { version = "1", features = ["full"] }
//! reqwest = { version = "0.11", features = ["json"] }
//! ```

use anyhow::anyhow;
use serde::Serialize;

#[derive(Serialize, Debug)]
struct Response {
    data: String,
}

fn main(url: String) -> anyhow::Result<Response> {
    let rt = tokio::runtime::Runtime::new()?;
    rt.block_on(async {
        let resp = reqwest::get(&url).await?.text().await?;
        Ok(Response { data: resp })
    })
}
```


# Snowflake

Arguments use `?` placeholders.

Name the parameters by adding comments before the statement:

```sql
-- ? name1 (text)
-- ? name2 (number) = 0
SELECT * FROM users WHERE name = ? AND age > ?;
```

## Receiving an S3Object as a script parameter

Declare the arg with type `(s3object)`. Windmill renders an S3 file picker for
it, downloads the file, and binds it as JSON text — Parquet/CSV files are
decoded server-side into a JSON array of records, JSON/JSONL pass through.
Wrap the bind with `PARSE_JSON(?)` and walk it with `LATERAL FLATTEN`:

```sql
-- ? file (s3object)
SELECT
  v.value:id::NUMBER AS id,
  v.value:name::STRING AS name
FROM LATERAL FLATTEN(input => PARSE_JSON(?)) v;
```

## Streaming query results to S3

Add a `-- s3` directive at the top of the script to stream the result set to S3
instead of returning rows. Windmill writes the file and returns its `S3Object`
as the script result.

```sql
-- s3 prefix=exports/users format=parquet
SELECT id, name FROM users;
```

All keys are optional: `prefix` (object key prefix), `storage` (named storage —
omit to use the workspace default), `format` (`json` (default), `parquet`, or
`csv`). Use this for large result sets — rows stream directly to S3 instead of
being buffered, bypassing the 10000-row return cap.


# TypeScript SDK (windmill-client)

Import: import * as wmill from 'windmill-client'

The client configures itself from the job's environment — base URL, token and credentials mode
are all set before your code runs, so there is nothing to initialize and no reason to read
WM_TOKEN or BASE_INTERNAL_URL and build an API URL yourself. Reconstructing that by hand only
reintroduces details the client already handles. Call the SDK for anything Windmill, and use raw
HTTP for third-party APIs.

The helpers below are the surface to prefer. For an endpoint none of them covers, import the
generated service classes (JobService, ScriptService, ...) from 'windmill-client' — they are not
listed here but they do exist. What does not exist is a helper name you guessed at: if it is
neither listed below nor a service method, do not call it.

To know who is running the script, read the contextual variables rather than calling the API:
`process.env.WM_END_USER_EMAIL || process.env.WM_EMAIL`. WM_END_USER_EMAIL is the app viewer when
the run was triggered from an app and empty otherwise (both variables are always defined), WM_EMAIL
is the user the job is permissioned as. WM_USERNAME is the matching username.

workerHasInternalServer(): boolean

/**
 * Initialize the Windmill client with authentication token and base URL
 * @param token - Authentication token (defaults to WM_TOKEN env variable)
 * @param baseUrl - API base URL (defaults to BASE_INTERNAL_URL or BASE_URL env variable)
 */
setClient(token?: string, baseUrl?: string): void

/**
 * Create a client configuration from env variables
 * @returns client configuration
 */
getWorkspace(): string

/**
 * Get a resource value by path
 * @param path path of the resource,  default to internal state path
 * @param undefinedIfEmpty if the resource does not exist, return undefined instead of throwing an error
 * @returns resource value
 */
async getResource(path?: string, undefinedIfEmpty?: boolean): Promise<any>

/**
 * Get the true root job id
 * @param jobId job id to get the root job id from (default to current job)
 * @returns root job id
 */
async getRootJobId(jobId?: string): Promise<string>

/**
 * Run a script synchronously by its path and wait for the result
 * @param path - Script path in Windmill
 * @param args - Arguments to pass to the script
 * @param verbose - Enable verbose logging
 * @param tag - Override the worker tag the job runs on
 * @returns Script execution result
 */
async runScriptByPath(path: string, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>

/**
 * Run a script synchronously by its hash and wait for the result
 * @param hash_ - Script hash in Windmill
 * @param args - Arguments to pass to the script
 * @param verbose - Enable verbose logging
 * @param tag - Override the worker tag the job runs on
 * @returns Script execution result
 */
async runScriptByHash(hash_: string, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>

/**
 * Append a text to the result stream
 * @param text text to append to the result stream
 */
appendToResultStream(text: string): void

/**
 * Stream to the result stream
 * @param stream stream to stream to the result stream
 */
async streamResult(stream: AsyncIterable<string>): Promise<void>

/**
 * Run a flow synchronously by its path and wait for the result
 * @param path - Flow path in Windmill
 * @param args - Arguments to pass to the flow
 * @param verbose - Enable verbose logging
 * @param tag - Override the worker tag the job runs on
 * @returns Flow execution result
 */
async runFlow(path: string | null = null, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>

/**
 * Wait for a job to complete and return its result
 * @param jobId - ID of the job to wait for
 * @param verbose - Enable verbose logging
 * @returns Job result when completed
 */
async waitJob(jobId: string, verbose: boolean = false): Promise<any>

/**
 * Get the result of a completed job
 * @param jobId - ID of the completed job
 * @returns Job result
 */
async getResult(jobId: string): Promise<any>

/**
 * Get the result of a job if completed, or its current status
 * @param jobId - ID of the job
 * @returns Object with started, completed, success, and result properties
 */
async getResultMaybe(jobId: string): Promise<any>

/**
 * Cancel a queued or running job by ID.
 * @param jobId - UUID of the job to cancel
 * @param reason - Optional reason for cancellation
 * @returns Response message from the cancel endpoint
 */
async cancelJob(jobId: string, reason: string | undefined = undefined): Promise<string>

/**
 * Run a script asynchronously by its path
 * @param path - Script path in Windmill
 * @param args - Arguments to pass to the script
 * @param scheduledInSeconds - Schedule execution for a future time (in seconds)
 * @param tag - Override the worker tag the job runs on
 * @returns Job ID of the created job
 */
async runScriptByPathAsync(path: string, args: Record<string, any> | null = null, scheduledInSeconds: number | null = null, tag: string | null = null): Promise<string>

/**
 * Run a script asynchronously by its hash
 * @param hash_ - Script hash in Windmill
 * @param args - Arguments to pass to the script
 * @param scheduledInSeconds - Schedule execution for a future time (in seconds)
 * @param tag - Override the worker tag the job runs on
 * @returns Job ID of the created job
 */
async runScriptByHashAsync(hash_: string, args: Record<string, any> | null = null, scheduledInSeconds: number | null = null, tag: string | null = null): Promise<string>

/**
 * Run a flow asynchronously by its path
 * @param path - Flow path in Windmill
 * @param args - Arguments to pass to the flow
 * @param scheduledInSeconds - Schedule execution for a future time (in seconds)
 * @param doNotTrackInParent - If false, tracks state in parent job (only use when fully awaiting the job)
 * @param tag - Override the worker tag the job runs on
 * @returns Job ID of the created job
 */
async runFlowAsync(path: string | null, args: Record<string, any> | null, scheduledInSeconds: number | null = null, // can only be set to false if this the job will be fully await and not concurrent with any other job // as otherwise the child flow and its own child will store their state in the parent job which will // lead to incorrectness and failures doNotTrackInParent: boolean = true, tag: string | null = null): Promise<string>

/**
 * Resolve a resource value in case the default value was picked because the input payload was undefined
 * @param obj resource value or path of the resource under the format `$res:path`
 * @returns resource value
 */
async resolveDefaultResource(obj: any): Promise<any>

/**
 * Get the state file path from environment variables
 * @returns State path string
 */
getStatePath(): string

/**
 * Set a resource value by path
 * @param path path of the resource to set, default to state path
 * @param value new value of the resource to set
 * @param initializeToTypeIfNotExist if the resource does not exist, initialize it with this type
 */
async setResource(value: any, path?: string, initializeToTypeIfNotExist?: string): Promise<void>

/**
 * Set the state
 * @param state state to set
 * @param path Optional state resource path override. Defaults to `getStatePath()`.
 */
async setState(state: any, path?: string): Promise<void>

/**
 * Set the progress
 * Progress cannot go back and limited to 0% to 99% range
 * @param percent Progress to set in %
 * @param jobId? Job to set progress for
 */
async setProgress(percent: number, jobId?: any): Promise<void>

/**
 * Get the progress
 * @param jobId? Job to get progress from
 * @returns Optional clamped between 0 and 100 progress value
 */
async getProgress(jobId?: any): Promise<number | null>

/**
 * Set a flow user state
 * @param key key of the state
 * @param value value of the state
 */
async setFlowUserState(key: string, value: any, errorIfNotPossible?: boolean): Promise<void>

/**
 * Get a flow user state
 * @param path path of the variable
 */
async getFlowUserState(key: string, errorIfNotPossible?: boolean): Promise<any>

/**
 * Get the state shared across executions
 * @param path Optional state resource path override. Defaults to `getStatePath()`.
 */
async getState(path?: string): Promise<any>

/**
 * Get a variable by path
 * @param path path of the variable
 * @returns variable value
 */
async getVariable(path: string): Promise<string>

/**
 * Set a variable by path, create if not exist
 * @param path path of the variable
 * @param value value of the variable
 * @param isSecretIfNotExist if the variable does not exist, create it as secret or not (default: false)
 * @param descriptionIfNotExist if the variable does not exist, create it with this description (default: "")
 */
async setVariable(path: string, value: string, isSecretIfNotExist?: boolean, descriptionIfNotExist?: string): Promise<void>

/**
 * Build a PostgreSQL connection URL from a database resource
 * @param path - Path to the database resource
 * @returns PostgreSQL connection URL string
 */
async databaseUrlFromResource(path: string): Promise<string>

async polarsConnectionSettings(s3_resource_path: string | undefined): Promise<any>

async duckdbConnectionSettings(s3_resource_path: string | undefined): Promise<any>

/**
 * Get S3 client settings from a resource or workspace default
 * @param s3_resource_path - Path to S3 resource (uses workspace default if undefined)
 * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)
 * @returns S3 client configuration settings
 */
async denoS3LightClientSettings(s3_resource_path: string | undefined, workspace: string | undefined = undefined): Promise<DenoS3LightClientSettings>

/**
 * Load the content of a file stored in S3. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.
 * 
 * ```typescript
 * let fileContent = await wmill.loadS3FileContent(inputFile)
 * // if the file is a raw text file, it can be decoded and printed directly:
 * const text = new TextDecoder().decode(fileContentStream)
 * console.log(text);
 * ```
 * 
 * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)
 */
async loadS3File(s3object: S3Object, s3ResourcePath: string | undefined = undefined, workspace: string | undefined = undefined): Promise<Uint8Array | undefined>

/**
 * Load the content of a file stored in S3 as a stream. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.
 * 
 * ```typescript
 * let fileContentBlob = await wmill.loadS3FileStream(inputFile)
 * // if the content is plain text, the blob can be read directly:
 * console.log(await fileContentBlob.text());
 * ```
 * 
 * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)
 */
async loadS3FileStream(s3object: S3Object, s3ResourcePath: string | undefined = undefined, workspace: string | undefined = undefined): Promise<Blob | undefined>

/**
 * Persist a file to the S3 bucket. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.
 * 
 * ```typescript
 * const s3object = await writeS3File(s3Object, "Hello Windmill!")
 * const fileContentAsUtf8Str = (await s3object.toArray()).toString('utf-8')
 * console.log(fileContentAsUtf8Str)
 * ```
 * 
 * @param workspace - Workspace to write to (defaults to the `WM_WORKSPACE` env var)
 */
async writeS3File(s3object: S3Object | undefined, fileContent: string | Blob, s3ResourcePath: string | undefined = undefined, contentType: string | undefined = undefined, contentDisposition: string | undefined = undefined, workspace: string | undefined = undefined): Promise<S3Object>

/**
 * Permanently delete a file from S3 by key.
 * 
 * ```typescript
 * await wmill.deleteS3File({ s3: "path/to/file.txt" })
 * ```
 * 
 * @param s3object - S3 object identifying the file to delete (must have `s3` set)
 * @param workspace - Workspace to delete from (defaults to the `WM_WORKSPACE` env var)
 */
async deleteS3File(s3object: S3Object, workspace: string | undefined = undefined): Promise<void>

/**
 * Sign S3 objects to be used by anonymous users in public apps
 * @param s3objects s3 objects to sign
 * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])
 * @returns signed s3 objects
 */
async signS3Objects(s3objects: S3Object[], { expirySecs }: { expirySecs?: number } = {}): Promise<S3Object[]>

/**
 * Sign S3 object to be used by anonymous users in public apps
 * @param s3object s3 object to sign
 * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])
 * @returns signed s3 object
 */
async signS3Object(s3object: S3Object, { expirySecs }: { expirySecs?: number } = {}): Promise<S3Object>

/**
 * Generate a presigned public URL for an array of S3 objects.
 * If an S3 object is not signed yet, it will be signed first.
 * @param s3Objects s3 objects to sign
 * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])
 * @returns list of signed public URLs
 */
async getPresignedS3PublicUrls(s3Objects: S3Object[], { baseUrl, expirySecs }: { baseUrl?: string; expirySecs?: number } = {}): Promise<string[]>

/**
 * Generate a presigned public URL for an S3 object. If the S3 object is not signed yet, it will be signed first.
 * @param s3Object s3 object to sign
 * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])
 * @returns signed public URL
 */
async getPresignedS3PublicUrl(s3Objects: S3Object, { baseUrl, expirySecs }: { baseUrl?: string; expirySecs?: number } = {}): Promise<string>

/**
 * Get URLs needed for resuming a flow after this step
 * @param approver approver name
 * @param flowLevel if true, generate resume URLs for the parent flow instead of the specific step.
 *                  This allows pre-approvals that can be consumed by any later suspend step in the same flow.
 * @returns approval page UI URL, resume and cancel API URLs for resuming the flow
 */
async getResumeUrls(approver?: string, flowLevel?: boolean): Promise<{
  approvalPage: string;
  resume: string;
  cancel: string;
}>

/**
 * Get an OIDC jwt token for auth to external services (e.g: Vault, AWS) (ee only)
 * @param audience audience of the token
 * @param expiresIn Optional number of seconds until the token expires
 * @returns jwt token
 */
async getIdToken(audience: string, expiresIn?: number): Promise<string>

/**
 * Convert a base64-encoded string to Uint8Array
 * @param data - Base64-encoded string
 * @returns Decoded Uint8Array
 */
base64ToUint8Array(data: string): Uint8Array

/**
 * Convert a Uint8Array to base64-encoded string
 * @param arrayBuffer - Uint8Array to encode
 * @returns Base64-encoded string
 */
uint8ArrayToBase64(arrayBuffer: Uint8Array): string

/**
 * Sends an interactive approval request via Slack, allowing optional customization of the message, approver, and form fields.
 * 
 * **[Enterprise Edition Only]** To include form fields in the Slack approval request, go to **Advanced -> Suspend -> Form**
 * and define a form. Learn more at [Windmill Documentation](https://www.windmill.dev/docs/flows/flow_approval#form).
 * 
 * @param {Object} options - The configuration options for the Slack approval request.
 * @param {string} options.slackResourcePath - The path to the Slack resource in Windmill.
 * @param {string} options.channelId - The Slack channel ID where the approval request will be sent.
 * @param {string} [options.message] - Optional custom message to include in the Slack approval request.
 * @param {string} [options.approver] - Optional user ID or name of the approver for the request.
 * @param {DefaultArgs} [options.defaultArgsJson] - Optional object defining or overriding the default arguments to a form field.
 * @param {Enums} [options.dynamicEnumsJson] - Optional object overriding the enum default values of an enum form field.
 * @param {string} [options.resumeButtonText] - Optional text for the resume button.
 * @param {string} [options.cancelButtonText] - Optional text for the cancel button.
 * 
 * @returns {Promise<void>} Resolves when the Slack approval request is successfully sent.
 * 
 * @throws {Error} If the function is not called within a flow or flow preview.
 * @throws {Error} If the `JobService.getSlackApprovalPayload` call fails.
 * 
 * **Usage Example:**
 * ```typescript
 * await requestInteractiveSlackApproval({
 *   slackResourcePath: "/u/alex/my_slack_resource",
 *   channelId: "admins-slack-channel",
 *   message: "Please approve this request",
 *   approver: "approver123",
 *   defaultArgsJson: { key1: "value1", key2: 42 },
 *   dynamicEnumsJson: { foo: ["choice1", "choice2"], bar: ["optionA", "optionB"] },
 *   resumeButtonText: "Resume",
 *   cancelButtonText: "Cancel",
 * });
 * ```
 * 
 * **Note:** This function requires execution within a Windmill flow or flow preview.
 */
async requestInteractiveSlackApproval({ slackResourcePath, channelId, message, approver, defaultArgsJson, dynamicEnumsJson, resumeButtonText, cancelButtonText, }: SlackApprovalOptions): Promise<void>

/**
 * Sends an interactive approval request via Teams, allowing optional customization of the message, approver, and form fields.
 * 
 * **[Enterprise Edition Only]** To include form fields in the Teams approval request, go to **Advanced -> Suspend -> Form**
 * and define a form. Learn more at [Windmill Documentation](https://www.windmill.dev/docs/flows/flow_approval#form).
 * 
 * @param {Object} options - The configuration options for the Teams approval request.
 * @param {string} options.teamName - The Teams team name where the approval request will be sent.
 * @param {string} options.channelName - The Teams channel name where the approval request will be sent.
 * @param {string} [options.message] - Optional custom message to include in the Teams approval request.
 * @param {string} [options.approver] - Optional user ID or name of the approver for the request.
 * @param {DefaultArgs} [options.defaultArgsJson] - Optional object defining or overriding the default arguments to a form field.
 * @param {Enums} [options.dynamicEnumsJson] - Optional object overriding the enum default values of an enum form field.
 * 
 * @returns {Promise<void>} Resolves when the Teams approval request is successfully sent.
 * 
 * @throws {Error} If the function is not called within a flow or flow preview.
 * @throws {Error} If the `JobService.getTeamsApprovalPayload` call fails.
 * 
 * **Usage Example:**
 * ```typescript
 * await requestInteractiveTeamsApproval({
 *   teamName: "admins-teams",
 *   channelName: "admins-teams-channel",
 *   message: "Please approve this request",
 *   approver: "approver123",
 *   defaultArgsJson: { key1: "value1", key2: 42 },
 *   dynamicEnumsJson: { foo: ["choice1", "choice2"], bar: ["optionA", "optionB"] },
 * });
 * ```
 * 
 * **Note:** This function requires execution within a Windmill flow or flow preview.
 */
async requestInteractiveTeamsApproval({ teamName, channelName, message, approver, defaultArgsJson, dynamicEnumsJson, }: TeamsApprovalOptions): Promise<void>

setWorkflowCtx(ctx: WorkflowCtx | null): void

async sleep(seconds: number): Promise<void>

/**
 * Execute `fn` inline and checkpoint the result. On replay the cached value is
 * returned without re-executing `fn`.
 * 
 * `fn`'s result is encoded as JSON and decoded back before it is returned, so
 * the round that runs the body sees the same types every replay sees: a `Date`
 * comes back as a string, a `Map` as `{}`. {@link Jsonified} is that shape.
 */
async step<T>(name: string, fn: () => T | Promise<T>,): Promise<Jsonified<Awaited<T>>>

/**
 * Create a task that dispatches to a separate Windmill script.
 * 
 * @example
 * const extract = taskScript("f/data/extract");
 * // inside workflow: await extract({ url: "https://..." })
 */
taskScript(path: string, options?: TaskOptions): (...args: any[]) => PromiseLike<any>

/**
 * Create a task that dispatches to a separate Windmill flow.
 * 
 * @example
 * const pipeline = taskFlow("f/etl/pipeline");
 * // inside workflow: await pipeline({ input: data })
 */
taskFlow(path: string, options?: TaskOptions): (...args: any[]) => PromiseLike<any>

/**
 * Mark an async function as a workflow-as-code entry point.
 * 
 * The function must be **deterministic**: given the same inputs it must call
 * tasks in the same order on every replay. Branching on task results is fine
 * (results are replayed from checkpoint), but branching on external state
 * (current time, random values, external API calls) must use `step()` to
 * checkpoint the value so replays see the same result.
 */
workflow<T>(fn: (...args: any[]) => Promise<T>): void

/**
 * Suspend the workflow and wait for an external approval.
 * 
 * Pass `key` to name the step, then `getApprovalUrls(key)` yields the URLs that
 * resume exactly this approval — route them through your own channel. Without a
 * key the steps are named `approval`, `approval_2`, ...
 * 
 * `skin: "minimal"` shows approvers only the request (form and approve/reject)
 * instead of the detailed page with the workflow's details. `description` is
 * shown above the form: a string, or a rich value such as `{ markdown: "..." }`.
 * 
 * @example
 * const urls = await step("urls", () => getApprovalUrls("manager"));
 * await step("notify", () => sendEmail(urls.resume, urls.cancel));
 * const { value, approver } = await waitForApproval({ key: "manager", timeout: 3600 });
 */
waitForApproval(options?: { timeout?: number; form?: object; selfApproval?: boolean; key?: string; skin?: "detailed" | "minimal"; description?: string | object; }): PromiseLike<{ value: any; approver: string; approved: boolean }>

/**
 * Resume/cancel/approval-page URLs bound to one `waitForApproval` step.
 * 
 * Unlike `getResumeUrls()`, which signs a random nonce, these address the very
 * `resume_job` record the step's built-in approval buttons use, so they are
 * stable across replays and safe to embed in a custom notification.
 * 
 * `stepKey` must match the `key` given to `waitForApproval`. Keys must be unique
 * within a workflow; reusing one throws rather than silently renaming it. The URL
 * only resumes while that step is awaiting approval; used at any other moment it is
 * rejected rather than banking a row a different approval would consume. Send it
 * ahead of time — approvers just cannot act before the workflow reaches the step.
 * 
 * `resume` and `cancel` are step-bound; `approvalPage` is not — it opens the job's
 * approval page, which acts on whichever approval is pending when it is used.
 * 
 * @example
 * const urls = await step("urls", () => getApprovalUrls("manager"));
 * await step("notify", () => sendEmail(urls.resume, urls.cancel));
 * await waitForApproval({ key: "manager" });
 */
async getApprovalUrls(stepKey: string = "approval", approver?: string): Promise<{
  approvalPage: string;
  resume: string;
  cancel: string;
}>

/**
 * Process items in parallel with optional concurrency control.
 * 
 * Each item is processed by calling `fn(item)`, which should be a task().
 * Items are dispatched in batches of `concurrency` (default: all at once).
 * 
 * @example
 * const process = task(async (item: string) => { ... });
 * const results = await parallel(items, process, { concurrency: 5 });
 */
async parallel<T, R>(items: T[], fn: (item: T) => PromiseLike<R> | R, options?: { concurrency?: number },): Promise<R[]>

/**
 * Commit Kafka offsets for a trigger with auto_commit disabled.
 * @param triggerPath - Path to the Kafka trigger (from event.wm_trigger.trigger_path)
 * @param topic - Kafka topic name (from event.topic)
 * @param partition - Partition number (from event.partition)
 * @param offset - Message offset to commit (from event.offset)
 */
async commitKafkaOffsets(triggerPath: string, topic: string, partition: number, offset: number,): Promise<void>

/**
 * Parse an S3 object from URI string or record format
 * @param s3Object - S3 object as URI string (`s3://storage/key`, `s3:///key`
 *   for the default storage) or record. Any other string throws rather than
 *   falling back to an auto-generated key: an auto key is requested by
 *   omitting the object, and a fallback would silently misplace the upload
 *   on any typo.
 * @returns S3 object record with storage and s3 key
 */
parseS3Object(s3Object: S3Object): S3ObjectRecord

/**
 * Create a SQL template function for PostgreSQL/datatable queries
 * @param name - Database/datatable name (default: "main")
 * @param opts.role - Connect as this data table role instead of the data table's default one.
 *   Only meaningful on a data table under roles, and only for a role you are a tenant of.
 * @returns SQL template function for building parameterized queries
 * @example
 * let sql = wmill.datatable()
 * let name = 'Robin'
 * let age = 21
 * await sql`
 *   SELECT * FROM friends
 *     WHERE name = ${name} AND age = ${age}::int
 * `.fetch()
 * @example
 * // Read through a restricted role
 * let sql = wmill.datatable("main", { role: "analytics" })
 */
datatable(name: string = "main", opts?: DatatableOptions): DatatableSqlTemplateFunction

/**
 * Create a SQL template function for DuckDB/ducklake queries
 * @param name - DuckDB database name, optionally with a schema as `name:schema` (default: "main")
 * @returns SQL template function for building parameterized queries
 * @example
 * let sql = wmill.ducklake()
 * let name = 'Robin'
 * let age = 21
 * await sql`
 *   SELECT * FROM friends
 *     WHERE name = ${name} AND age = ${age}
 * `.fetch()
 * @example
 * // Target a specific schema within the ducklake
 * let sql = wmill.ducklake("my_lake:analytics")
 */
ducklake(name: string = "main"): SqlTemplateFunction

/**
 * Idempotently materialize `selectSql` into a ducklake table for one
 * partition (or the whole table when `partition` is omitted) — the client-side
 * equivalent of the `// materialize` engine.
 * With `uniqueKey` it upserts the slice (delete-by-key + insert); otherwise it
 * replaces it (whole table → `CREATE OR REPLACE`; partition → delete + insert).
 * Safe to re-run for the same partition (backfill / failure-recovery).
 * 
 * Returns a lazy statement — call `.execute()` to run it:
 * `await wmill.upsertPartition({ table, selectSql, partition }).execute()`.
 */
upsertPartition(opts: DucklakeMaterializeOptions): SqlStatement<any>

/**
 * INSERT-only materialization (no dedup/replace) for append-only tables.
 * Re-running the same partition duplicates rows — use only for immutable
 * event-log sources.
 * 
 * Returns a lazy statement — call `.execute()` to run it:
 * `await wmill.appendPartition({ table, selectSql, partition }).execute()`.
 */
appendPartition(opts: Omit<DucklakeMaterializeOptions, "uniqueKey">,): SqlStatement<any>


# Python SDK (wmill)

Import: import wmill

The client configures itself from the job's environment — base URL, token and credentials mode
are all set before your code runs, so there is nothing to initialize and no reason to read
WM_TOKEN or BASE_INTERNAL_URL and build an API URL yourself. Reconstructing that by hand only
reintroduces details the client already handles. Call the SDK for anything Windmill, and use raw
HTTP for third-party APIs.

The functions below are the surface to prefer. For an endpoint none of them covers,
wmill.Windmill().get(endpoint) and .post(endpoint) issue an authenticated request against this
instance. What does not exist is a function name you guessed at: if it is not listed below, do
not call it.

To know who is running the script, read the contextual variables rather than calling the API:
`os.environ.get("WM_END_USER_EMAIL") or os.environ.get("WM_EMAIL")`. WM_END_USER_EMAIL is the app
viewer when the run was triggered from an app and empty otherwise (both variables are always
defined), WM_EMAIL is the user the job is permissioned as. WM_USERNAME is the matching username.

def worker_has_internal_server() -> bool

def get_mocked_api() -> Optional[dict]

# Get the HTTP client instance.
# 
# Returns:
#     Configured httpx.Client for API requests
def get_client() -> httpx.Client

# Make an HTTP GET request to the Windmill API.
# 
# Args:
#     endpoint: API endpoint path
#     raise_for_status: Whether to raise an exception on HTTP errors
#     **kwargs: Additional arguments passed to httpx.get
# 
# Returns:
#     HTTP response object
def get(endpoint, raise_for_status = True, **kwargs) -> httpx.Response

# Make an HTTP POST request to the Windmill API.
# 
# Args:
#     endpoint: API endpoint path
#     raise_for_status: Whether to raise an exception on HTTP errors
#     **kwargs: Additional arguments passed to httpx.post
# 
# Returns:
#     HTTP response object
def post(endpoint, raise_for_status = True, **kwargs) -> httpx.Response

# Create a new authentication token.
# 
# Args:
#     duration: Token validity duration (default: 1 day)
# 
# Returns:
#     New authentication token string
def create_token(duration = dt.timedelta(days=1)) -> str

# Create a script job by path and return its job id.
def run_script_by_path_async(path: str, args: dict = None, scheduled_in_secs: int = None, tag: str = None) -> str

# Create a script job by hash and return its job id.
def run_script_by_hash_async(hash_: str, args: dict = None, scheduled_in_secs: int = None, tag: str = None) -> str

# Create a flow job and return its job id.
def run_flow_async(path: str, args: dict = None, scheduled_in_secs: int = None, do_not_track_in_parent: bool = True, tag: str = None) -> str

# Run script by path synchronously and return its result.
def run_script_by_path(path: str, args: dict = None, timeout: dt.timedelta | int | float | None = None, verbose: bool = False, cleanup: bool = True, assert_result_is_not_none: bool = False, tag: str = None) -> Any

# Run script by hash synchronously and return its result.
def run_script_by_hash(hash_: str, args: dict = None, timeout: dt.timedelta | int | float | None = None, verbose: bool = False, cleanup: bool = True, assert_result_is_not_none: bool = False, tag: str = None) -> Any

# Run a script on the current worker without creating a job.
# 
# On agent workers (no internal server), falls back to running a normal
# preview job and waiting for the result.
def run_inline_script_preview(content: str, language: str, args: dict = None) -> Any

# Wait for a job to complete and return its result.
# 
# Args:
#     job_id: ID of the job to wait for
#     timeout: Maximum time to wait (seconds or timedelta)
#     verbose: Enable verbose logging
#     cleanup: Register cleanup handler to cancel job on exit
#     assert_result_is_not_none: Raise exception if result is None
# 
# Returns:
#     Job result when completed
# 
# Raises:
#     TimeoutError: If timeout is reached
#     Exception: If job fails
def wait_job(job_id, timeout: dt.timedelta | int | float | None = None, verbose: bool = False, cleanup: bool = True, assert_result_is_not_none: bool = False)

# Cancel a specific job by ID.
# 
# Args:
#     job_id: UUID of the job to cancel
#     reason: Optional reason for cancellation
# 
# Returns:
#     Response message from the cancel endpoint
def cancel_job(job_id: str, reason: str = None) -> str

# Cancel currently running executions of the same script.
def cancel_running() -> dict

# Get job details by ID.
# 
# Args:
#     job_id: UUID of the job
# 
# Returns:
#     Job details dictionary
def get_job(job_id: str) -> dict

# Get the root job ID for a flow hierarchy.
# 
# Args:
#     job_id: Job ID (defaults to current WM_JOB_ID)
# 
# Returns:
#     Root job ID
def get_root_job_id(job_id: str | None = None) -> dict

# Get an OIDC JWT token for authentication to external services.
# 
# Args:
#     audience: Token audience (e.g., "vault", "aws")
#     expires_in: Optional expiration time in seconds
# 
# Returns:
#     JWT token string
def get_id_token(audience: str, expires_in: int | None = None) -> str

# Get the status of a job.
# 
# Args:
#     job_id: UUID of the job
# 
# Returns:
#     Job status: "RUNNING", "WAITING", or "COMPLETED"
def get_job_status(job_id: str) -> JobStatus

# Get the result of a completed job.
# 
# Args:
#     job_id: UUID of the completed job
#     assert_result_is_not_none: Raise exception if result is None
# 
# Returns:
#     Job result
def get_result(job_id: str, assert_result_is_not_none: bool = True) -> Any

# Get a variable value by path.
# 
# Args:
#     path: Variable path in Windmill
# 
# Returns:
#     Variable value as string
def get_variable(path: str) -> str

# Set a variable value by path, creating it if it doesn't exist.
# 
# Args:
#     path: Variable path in Windmill
#     value: Variable value to set
#     is_secret: Whether the variable should be secret (default: False)
def set_variable(path: str, value: str, is_secret: bool = False) -> None

# Get a resource value by path.
# 
# Args:
#     path: Resource path in Windmill
#     none_if_undefined: Return None instead of raising if not found
#     interpolated: if variables and resources are fully unrolled
# 
# Returns:
#     Resource value dictionary or None
def get_resource(path: str, none_if_undefined: bool = False, interpolated: bool = True) -> dict | None

# Set a resource value by path, creating it if it doesn't exist.
# 
# Args:
#     value: Resource value to set
#     path: Resource path in Windmill
#     resource_type: Resource type for creation
def set_resource(value: Any, path: str, resource_type: str)

# List resources from Windmill workspace.
# 
# Args:
#     resource_type: Optional resource type to filter by (e.g., "postgresql", "mysql", "s3")
#     page: Optional page number for pagination
#     per_page: Optional number of results per page
#     
# Returns:
#     List of resource dictionaries
def list_resources(resource_type: str = None, page: int = None, per_page: int = None) -> list[dict]

# Set the workflow state.
# 
# Args:
#     value: State value to set
#     path: Optional state resource path override.
def set_state(value: Any, path: str | None = None) -> None

# Get the workflow state.
# 
# Args:
#     path: Optional state resource path override.
# 
# Returns:
#     State value or None if not set
def get_state(path: str | None = None) -> Any

# Set job progress percentage (0-99).
# 
# Args:
#     value: Progress percentage
#     job_id: Job ID (defaults to current WM_JOB_ID)
def set_progress(value: int, job_id: Optional[str] = None)

# Get job progress percentage.
# 
# Args:
#     job_id: Job ID (defaults to current WM_JOB_ID)
# 
# Returns:
#     Progress value (0-100) or None if not set
def get_progress(job_id: Optional[str] = None) -> Any

# Set the user state of a flow at a given key
def set_flow_user_state(key: str, value: Any) -> None

# Get the user state of a flow at a given key
def get_flow_user_state(key: str) -> Any

# Get the Windmill server version.
# 
# Returns:
#     Version string
def version()

# Convenient helpers that takes an S3 resource as input and returns the settings necessary to
# initiate an S3 connection from DuckDB
def get_duckdb_connection_settings(s3_resource_path: str = '') -> DuckDbConnectionSettings | None

# Convenient helpers that takes an S3 resource as input and returns the settings necessary to
# initiate an S3 connection from Polars
def get_polars_connection_settings(s3_resource_path: str = '') -> PolarsConnectionSettings

# Convenient helpers that takes an S3 resource as input and returns the settings necessary to
# initiate an S3 connection using boto3
def get_boto3_connection_settings(s3_resource_path: str = '') -> Boto3ConnectionSettings

# Load a file from the workspace s3 bucket and returns its content as bytes.
# 
# '''python
# from wmill import S3Object
# 
# s3_obj = S3Object(s3="/path/to/my_file.txt")
# my_obj_content = client.load_s3_file(s3_obj)
# file_content = my_obj_content.decode("utf-8")
# '''
def load_s3_file(s3object: S3Object | str, s3_resource_path: str | None) -> bytes

# Load a file from the workspace s3 bucket and returns the bytes stream.
# 
# '''python
# from wmill import S3Object
# 
# s3_obj = S3Object(s3="/path/to/my_file.txt")
# with wmill.load_s3_file_reader(s3object, s3_resource_path) as file_reader:
#     print(file_reader.read())
# '''
def load_s3_file_reader(s3object: S3Object | str, s3_resource_path: str | None) -> BufferedReader

# Write a file to the workspace S3 bucket
# 
# '''python
# from wmill import S3Object
# 
# s3_obj = S3Object(s3="/path/to/my_file.txt")
# 
# # for an in memory bytes array:
# file_content = b'Hello Windmill!'
# client.write_s3_file(s3_obj, file_content)
# 
# # for a file:
# with open("my_file.txt", "rb") as my_file:
#     client.write_s3_file(s3_obj, my_file)
# '''
def write_s3_file(s3object: S3Object | str | None, file_content: BufferedReader | bytes, s3_resource_path: str | None, content_type: str | None = None, content_disposition: str | None = None) -> S3Object

# Permanently delete a file from the workspace S3 bucket.
# 
# '''python
# from wmill import S3Object
# 
# s3_obj = S3Object(s3="/path/to/my_file.txt")
# client.delete_s3_object(s3_obj)
# '''
def delete_s3_object(s3object: S3Object | str, s3_resource_path: str | None = None) -> None

# Sign S3 objects for use by anonymous users in public apps.
# 
# Args:
#     s3_objects: List of S3 objects to sign
#     expiry_secs: How long the signature stays valid, in seconds
#         (defaults to 43200 = 12h, clamped to [60, 604800])
# 
# Returns:
#     List of signed S3 objects
def sign_s3_objects(s3_objects: list[S3Object | str], expiry_secs: int | None = None) -> list[S3Object]

# Sign a single S3 object for use by anonymous users in public apps.
# 
# Args:
#     s3_object: S3 object to sign
#     expiry_secs: How long the signature stays valid, in seconds
#         (defaults to 43200 = 12h, clamped to [60, 604800])
# 
# Returns:
#     Signed S3 object
def sign_s3_object(s3_object: S3Object | str, expiry_secs: int | None = None) -> S3Object

# Generate presigned public URLs for an array of S3 objects.
# If an S3 object is not signed yet, it will be signed first.
# 
# Args:
#     s3_objects: List of S3 objects to sign
#     base_url: Optional base URL for the presigned URLs (defaults to WM_BASE_URL)
#     expiry_secs: How long the signatures stay valid, in seconds
#         (defaults to 43200 = 12h, clamped to [60, 604800])
# 
# Returns:
#     List of signed public URLs
# 
# Example:
#     >>> s3_objs = [S3Object(s3="/path/to/file1.txt"), S3Object(s3="/path/to/file2.txt")]
#     >>> urls = client.get_presigned_s3_public_urls(s3_objs)
def get_presigned_s3_public_urls(s3_objects: list[S3Object | str], base_url: str | None = None, expiry_secs: int | None = None) -> list[str]

# Generate a presigned public URL for an S3 object.
# If the S3 object is not signed yet, it will be signed first.
# 
# Args:
#     s3_object: S3 object to sign
#     base_url: Optional base URL for the presigned URL (defaults to WM_BASE_URL)
#     expiry_secs: How long the signature stays valid, in seconds
#         (defaults to 43200 = 12h, clamped to [60, 604800])
# 
# Returns:
#     Signed public URL
# 
# Example:
#     >>> s3_obj = S3Object(s3="/path/to/file.txt")
#     >>> url = client.get_presigned_s3_public_url(s3_obj)
def get_presigned_s3_public_url(s3_object: S3Object | str, base_url: str | None = None, expiry_secs: int | None = None) -> str

# Get the current user information.
# 
# Returns:
#     User details dictionary
def whoami() -> dict

# Get the current user information (alias for whoami).
# 
# Returns:
#     User details dictionary
def user() -> dict

# Get the state resource path from environment.
# 
# Returns:
#     State path string
def state_path() -> str

# Get the workflow state.
# 
# Returns:
#     State value or None if not set
def state() -> Any

# Set the state in the shared folder using pickle
def set_shared_state_pickle(value: Any, path: str = 'state.pickle') -> None

# Get the state in the shared folder using pickle
def get_shared_state_pickle(path: str = 'state.pickle') -> Any

# Set the state in the shared folder using pickle
def set_shared_state(value: Any, path: str = 'state.json') -> None

# Get the state in the shared folder using pickle
def get_shared_state(path: str = 'state.json') -> None

# Get URLs needed for resuming a flow after suspension.
# 
# Args:
#     approver: Optional approver name
#     flow_level: If True, generate resume URLs for the parent flow instead of the
#         specific step. This allows pre-approvals that can be consumed by any later
#         suspend step in the same flow.
# 
# Returns:
#     Dictionary with approvalPage, resume, and cancel URLs
def get_resume_urls(approver: str = None, flow_level: bool = None) -> dict

# Get the resume URLs bound to one ``wait_for_approval`` step of this workflow.
# 
# Args:
#     step_key: Checkpoint key of the approval step, as passed to
#         ``wait_for_approval(key=...)``
#     approver: Optional approver name
# 
# Returns:
#     Dictionary with approvalPage, resume, and cancel URLs
def get_approval_urls(step_key: str = 'approval', approver: str = None) -> dict

# Sends an interactive approval request via Slack, allowing optional customization of the message, approver, and form fields.
# 
# **[Enterprise Edition Only]** To include form fields in the Slack approval request, use the "Advanced -> Suspend -> Form" functionality.
# Learn more at: https://www.windmill.dev/docs/flows/flow_approval#form
# 
# :param slack_resource_path: The path to the Slack resource in Windmill.
# :type slack_resource_path: str
# :param channel_id: The Slack channel ID where the approval request will be sent.
# :type channel_id: str
# :param message: Optional custom message to include in the Slack approval request.
# :type message: str, optional
# :param approver: Optional user ID or name of the approver for the request.
# :type approver: str, optional
# :param default_args_json: Optional dictionary defining or overriding the default arguments for form fields.
# :type default_args_json: dict, optional
# :param dynamic_enums_json: Optional dictionary overriding the enum default values of enum form fields.
# :type dynamic_enums_json: dict, optional
# 
# :raises Exception: If the function is not called within a flow or flow preview.
# :raises Exception: If the required flow job or flow step environment variables are not set.
# 
# :return: None
# 
# **Usage Example:**
#     >>> client.request_interactive_slack_approval(
#     ...     slack_resource_path="/u/alex/my_slack_resource",
#     ...     channel_id="admins-slack-channel",
#     ...     message="Please approve this request",
#     ...     approver="approver123",
#     ...     default_args_json={"key1": "value1", "key2": 42},
#     ...     dynamic_enums_json={"foo": ["choice1", "choice2"], "bar": ["optionA", "optionB"]},
#     ... )
# 
# **Notes:**
# - This function must be executed within a Windmill flow or flow preview.
# - The function checks for required environment variables (`WM_FLOW_JOB_ID`, `WM_FLOW_STEP_ID`) to ensure it is run in the appropriate context.
def request_interactive_slack_approval(slack_resource_path: str, channel_id: str, message: str = None, approver: str = None, default_args_json: dict = None, dynamic_enums_json: dict = None) -> None

# Send a message to a Microsoft Teams conversation with conversation_id, where success is used to style the message
def send_teams_message(conversation_id: str, text: str, success: bool = True, card_block: dict = None)

# Get a DataTable client for SQL queries.
# 
# Args:
#     name: Database name (default: "main")
#     role: Connect as this data table role instead of the data table's default one.
#         Only meaningful on a data table under roles, and only for a role you are a
#         tenant of.
# 
# Returns:
#     DataTableClient instance
def datatable(name: str = 'main', *, role: Optional[str] = None)

# Get a DuckLake client for DuckDB queries.
# 
# Args:
#     name: Database name (default: "main")
# 
# Returns:
#     DucklakeClient instance
def ducklake(name: str = 'main')

def init_global_client(f)

def deprecate(in_favor_of: str)

# Get the current workspace ID.
# 
# Returns:
#     Workspace ID string
def get_workspace() -> str

def get_version() -> str

# Create a script job and return its job ID.
# 
# Args:
#     hash_or_path: Script hash or path (determined by presence of '/')
#     args: Script arguments
#     scheduled_in_secs: Delay before execution in seconds
#     tag: Override the worker tag the job runs on
# 
# Returns:
#     Job ID string
def run_script_async(hash_or_path: str, args: Dict[str, Any] = None, scheduled_in_secs: int = None, tag: str = None) -> str

# Run a script synchronously by hash and return its result.
# 
# Args:
#     hash: Script hash
#     args: Script arguments
#     verbose: Enable verbose logging
#     assert_result_is_not_none: Raise exception if result is None
#     cleanup: Register cleanup handler to cancel job on exit
#     timeout: Maximum time to wait
#     tag: Override the worker tag the job runs on
# 
# Returns:
#     Script result
def run_script_sync(hash: str, args: Dict[str, Any] = None, verbose: bool = False, assert_result_is_not_none: bool = True, cleanup: bool = True, timeout: dt.timedelta = None, tag: str = None) -> Any

# Run a script synchronously by path and return its result.
# 
# Args:
#     path: Script path
#     args: Script arguments
#     verbose: Enable verbose logging
#     assert_result_is_not_none: Raise exception if result is None
#     cleanup: Register cleanup handler to cancel job on exit
#     timeout: Maximum time to wait
#     tag: Override the worker tag the job runs on
# 
# Returns:
#     Script result
def run_script_by_path_sync(path: str, args: Dict[str, Any] = None, verbose: bool = False, assert_result_is_not_none: bool = True, cleanup: bool = True, timeout: dt.timedelta = None, tag: str = None) -> Any

# Convenient helpers that takes an S3 resource as input and returns the settings necessary to
# initiate an S3 connection from DuckDB
def duckdb_connection_settings(s3_resource_path: str = '') -> DuckDbConnectionSettings

# Convenient helpers that takes an S3 resource as input and returns the settings necessary to
# initiate an S3 connection from Polars
def polars_connection_settings(s3_resource_path: str = '') -> PolarsConnectionSettings

# Convenient helpers that takes an S3 resource as input and returns the settings necessary to
# initiate an S3 connection using boto3
def boto3_connection_settings(s3_resource_path: str = '') -> Boto3ConnectionSettings

# Get the state resource path from environment.
# 
# Returns:
#     State path string
def get_state_path() -> str

# Parse resource syntax from string.
def parse_resource_syntax(s: str) -> Optional[str]

# Parse S3 object from a `s3://<storage>/<key>` URI string (`s3:///<key>`
# for the default storage) or S3Object format. Any other string raises
# rather than falling back to an auto-generated key: an auto key is
# requested by omitting the object, and a fallback would silently misplace
# the upload on any typo.
def parse_s3_object(s3_object: S3Object | str) -> S3Object

# Parse variable syntax from string.
def parse_variable_syntax(s: str) -> Optional[str]

# Append a text to the result stream.
# 
# Args:
#     text: text to append to the result stream
def append_to_result_stream(text: str) -> None

# Stream to the result stream.
# 
# Args:
#     stream: stream to stream to the result stream
def stream_result(stream) -> None

# Execute a SQL query against the DataTable.
# 
# Args:
#     sql: SQL query string with $1, $2, etc. placeholders
#     *args: Positional arguments to bind to query placeholders
# 
# Returns:
#     SqlQuery instance for fetching results
def query(sql: str, *args) -> SqlQuery

# Idempotently materialize the rows of `select_sql` into ducklake
# `table` for one `partition` (or the whole table when `partition` is
# None). Client-side equivalent of the `// materialize` engine: with
# `unique_key` it upserts within the slice (delete-by-key + insert);
# without it, it replaces (whole table → CREATE OR REPLACE; partition →
# delete the partition + insert). Re-running the same slice is safe — the
# backfill / failure-recovery contract.
# 
# The partition value is bound as a DuckDB arg (never string-interpolated)
# so it cannot inject SQL. `select_sql` is trusted (your own query).
def upsert_partition(table: str, select_sql: str, partition: str = None, unique_key: str = None, partition_col: str = '_wm_partition', schema: str = None)

# INSERT-only materialization (no dedup / no replace) for an immutable
# event-log table — for one `partition`, or the whole table when
# `partition` is None. NOTE: unlike `upsert_partition`, re-running the same
# slice duplicates rows — use only for append-only sources.
def append_partition(table: str, select_sql: str, partition: str = None, partition_col: str = '_wm_partition', schema: str = None)

# Read a materialized ducklake table, optionally a single partition.
def read(table: str, partition: str = None, partition_col: str = '_wm_partition', schema: str = None)

# Execute query and fetch results.
# 
# Args:
#     result_collection: Optional result collection mode
# 
# Returns:
#     Query results
def fetch(result_collection: str | None = None)

# Execute query and fetch first row of results.
# 
# Returns:
#     First row of query results
def fetch_one()

# Execute query and fetch first row of results. Return result as a scalar value.
# 
# Returns:
#     First row of query result as a scalar value
def fetch_one_scalar()

# Execute query and don't return any results.
#         
def execute()

# DuckDB executor requires explicit argument types at declaration
# These types exist in both DuckDB and Postgres
# Check that the types exist if you plan to extend this function for other SQL engines.
def infer_sql_type(value) -> str

def parse_sql_client_name(name: str) -> tuple[str, Optional[str]]

# Decorator that marks a function as a workflow task.
# 
# Works in both WAC v1 (sync, HTTP-based dispatch) and WAC v2
# (async, checkpoint/replay) modes:
# 
# - **v2 (inside @workflow)**: dispatches as a checkpoint step.
# - **v1 (WM_JOB_ID set, no @workflow)**: dispatches via HTTP API.
# - **Standalone**: executes the function body directly.
# 
# A task runs as its own job, so its result is always encoded as JSON and
# decoded back before the caller sees it: a ``datetime`` comes back as a
# string, a tuple as a list.
# 
# ``retry`` re-dispatches the task after a failure, inside ``@workflow`` only.
# Every attempt is a step of its own (``call_api``, ``call_api#2``, ...) and
# the wait between two of them is a durable sleep, so a retrying task holds no
# worker while it backs off. Keys: ``attempts`` (retries after the first
# failure, a whole number from 0 to 100), ``delay`` (seconds before the first
# retry, sub-second delays dropped), ``multiplier`` (applied to the delay
# after each attempt, 1 keeps it constant), ``max_delay`` (ceiling in
# seconds). ``attempts`` is required, and an out-of-range or unknown key is
# rejected where the policy is written.
# 
# A workflow sleeps once per round, so tasks backing off in the same fan-out
# wait one after another rather than together: the delay before a fan-out
# retries is the sum of every backoff pending in it, not the longest one, and
# it grows with both the width of the fan-out and ``attempts``. Retries with
# no ``delay`` all go out in a single round.
# 
# ``cache_ttl`` serves a previous result of the task for that many seconds
# instead of running it again. A task is keyed on its step key (its name and
# call order) and the workflow's input, not on the arguments it is called
# with, so cache one only when whether it runs, and what it receives, follow
# from the workflow's input alone. A ``task_script`` target is keyed on the
# arguments it is called with. It has no effect on a ``task_flow`` target,
# which keeps its flow's own cache policy.
# 
# Usage::
# 
#     @task
#     async def extract_data(url: str): ...
# 
#     @task(path="f/external_script", timeout=600, tag="gpu")
#     async def run_external(x: int): ...
# 
#     @task(retry={"attempts": 3, "delay": 30, "multiplier": 2})
#     async def call_api(payload: dict): ...
def task(_func = None, *, path: Optional[str] = None, tag: Optional[str] = None, timeout: Optional[int] = None, cache_ttl: Optional[int] = None, priority: Optional[int] = None, concurrency_limit: Optional[int] = None, concurrency_key: Optional[str] = None, concurrency_time_window_s: Optional[int] = None, retry: Optional[dict] = None)

# Create a task that dispatches to a separate Windmill script.
# 
# ``retry`` takes the same policy as :func:`task`.
# 
# Usage::
# 
#     extract = task_script("f/data/extract", timeout=600)
# 
#     @workflow
#     async def main():
#         data = await extract(url="https://...")
def task_script(path: str, *, timeout: Optional[int] = None, tag: Optional[str] = None, cache_ttl: Optional[int] = None, priority: Optional[int] = None, concurrency_limit: Optional[int] = None, concurrency_key: Optional[str] = None, concurrency_time_window_s: Optional[int] = None, retry: Optional[dict] = None)

# Create a task that dispatches to a separate Windmill flow.
# 
# ``retry`` takes the same policy as :func:`task`.
# 
# Usage::
# 
#     pipeline = task_flow("f/etl/pipeline", priority=10)
# 
#     @workflow
#     async def main():
#         result = await pipeline(input=data)
def task_flow(path: str, *, timeout: Optional[int] = None, tag: Optional[str] = None, cache_ttl: Optional[int] = None, priority: Optional[int] = None, concurrency_limit: Optional[int] = None, concurrency_key: Optional[str] = None, concurrency_time_window_s: Optional[int] = None, retry: Optional[dict] = None)

# Decorator marking an async function as a workflow-as-code entry point.
# 
# The function must be **deterministic**: given the same inputs it must call
# tasks in the same order on every replay. Branching on task results is fine
# (results are replayed from checkpoint), but branching on external state
# (current time, random values, external API calls) must use ``step()`` to
# checkpoint the value so replays see the same result.
def workflow(func)

# Execute ``fn`` inline and checkpoint the result.
# 
# On replay the cached value is returned without re-executing ``fn``.
# Use for lightweight deterministic operations (timestamps, random IDs,
# config reads) that should not incur the overhead of a child job.
# 
# ``fn``'s result is encoded as JSON and decoded back before it is returned,
# so the round that runs the body sees the same types every replay sees:
# a ``datetime`` comes back as a string, a tuple as a list.
async def step(name: str, fn)

# Server-side sleep — suspend the workflow for the given duration without holding a worker.
# 
# Inside a @workflow, the parent job suspends and auto-resumes after ``seconds``.
# Outside a workflow, falls back to ``asyncio.sleep``.
async def sleep(seconds: int)

# Suspend the workflow and wait for an external approval.
# 
# Pass ``key`` to name the step, then ``get_approval_urls(key)`` yields the URLs
# that resume exactly this approval — route them through your own channel.
# Without a key the steps are named ``approval``, ``approval_2``, ...
# 
# Returns a dict with ``value`` (form data), ``approver``, and ``approved``.
# 
# Args:
#     timeout: Approval timeout in seconds (default 1800).
#     form: Optional form schema for the approval page.
#     self_approval: Whether the user who triggered the flow can approve it (default True).
#     key: Optional checkpoint key naming this approval step.
#     skin: ``"minimal"`` shows approvers only the request (form and approve/reject)
#         instead of the detailed page with the workflow's details.
#     description: Shown to approvers above the form: a string, or a rich value such as
#         ``{"markdown": "..."}``.
# 
# Example::
# 
#     urls = await step("urls", lambda: get_approval_urls("manager"))
#     await step("notify", lambda: send_email(urls["resume"], urls["cancel"]))
#     result = await wait_for_approval(key="manager", timeout=3600)
async def wait_for_approval(timeout: int = 1800, form: dict | None = None, self_approval: bool = True, key: str | None = None, skin: Literal['detailed', 'minimal'] | None = None, description: str | dict | None = None) -> dict

# Process items in parallel with optional concurrency control.
# 
# Each item is processed by calling ``fn(item)``, which should be a @task.
# Items are dispatched in batches of ``concurrency`` (default: all at once).
# 
# Example::
# 
#     @task
#     async def process(item: str):
#         ...
# 
#     results = await parallel(items, process, concurrency=5)
async def parallel(items, fn, *, concurrency: Optional[int] = None)

# Commit Kafka offsets for a trigger with auto_commit disabled.
# 
# Args:
#     trigger_path: Path to the Kafka trigger (from event['wm_trigger']['trigger_path'])
#     topic: Kafka topic name (from event['topic'])
#     partition: Partition number (from event['partition'])
#     offset: Message offset to commit (from event['offset'])
def commit_kafka_offsets(trigger_path: str, topic: str, partition: int, offset: int) -> None