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Docusaurus 3 (windmill-labs#513)
* Upgraded to Docusaurus 3 * Fix prop names * Fix all errors * Improving lighthouse score * Fix broken links * Fix broken links * improve seo + accessibitlity * Restore swizzled components * Fix build * Add missing titles * Fix blog posts + restore broken feature * fix(frontend): Fix footer * fix(frontend): Fix inkeep design
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‎blog/2023-06-05-discord-bot/index.mdx

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[Workflows](/docs/flows/flow_editor) are graphs ([DAG](https://en.wikipedia.org/wiki/Directed_acyclic_graph)) that represent the dependencies between steps.
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The goal of an orchestrator or workflow engine is to execute the steps on different workers that respect that graph's dependencies, and pass the required data defined in the steps' inputs while applying parallelism when possible.
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<br />
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In Windmill, each step is [code](/docs/getting_started/scripts_quickstart) with a main function in TypeScript, Python, Go, Bash or raw queries to databases.
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Notice that the script takes a special parameter `gh_auth` which is a Windmill resource that contains the [GitHub token](/docs/integrations/github).
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<div class="grid grid-cols-2 gap-6 mb-4">
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<div className="grid grid-cols-2 gap-6 mb-4">
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<DocCard
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title="Resources"
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description="Resources are rich objects in JSON that allow to store configuration and credentials."
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#### Learn more about Windmill features
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<div className="grid grid-cols-2 gap-6 mb-4">
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<DocCard title="For loops" description="For loops documentation" href="/docs/flows/flow_loops" />
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<DocCard
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title="Schedule"
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#### Learn more about approval steps
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<DocCard
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title="Suspend & Approval / Prompts"
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description="Flows can be suspended until resumed or cancelled event(s) are received"

‎blog/2023-08-04-windmill-ai/index.mdx

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You write code as queried by the user. Only output code. Wrap the code like that:
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```language
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{code}
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\{code\}
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```
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Put explanations directly in the code as comments.
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For the user message, we use the following template (here for Python):
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```
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Write a function in python called "main". The function should {description}.
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Write a function in python called "main". The function should \{description\}.
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Specify the parameter types. Do not call the main function.
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```
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The {description} placeholder gets replaced by the user's instructions.
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The \{description\} placeholder gets replaced by the user's instructions.
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In addition, we simply instruct the model to fit Windmill's execution model: a main function with typed parameters used to infer the script's inputs.
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### Handling resource types
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Below is the updated template for Python with sample resource types:
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```
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Write a function in python called "main". The function should {description}.
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Write a function in python called "main". The function should \{description\}.
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Specify the parameter types. Do not call the main function.
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You have access to the following resource types.
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Below is the corresponding prompt template:
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```
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Write SQL code for PostgreSQL that should \{description\}.
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Arguments can be obtained directly in the statement with `$1::{type}`, `$2::{type}`, etc...
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Name the parameters by adding comments before the command like that:
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`-- $1 name1` or `-- $2 name = default` (one per row, do not include the type)
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Below is the updated template, accompanied by part of the schema from a Windmill instance database as an example:
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```
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Write SQL code for PostgreSQL that should {description}.
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Write SQL code for PostgreSQL that should \{description\}.
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Arguments can be obtained directly in the statement with `$1::{type}`, `$2::{type}`, etc...
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Name the parameters by adding comments before the command like that:
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`-- $1 name1` or `-- $2 name = default` (one per row, do not include the type)
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````
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Here's my python3 code:
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```python
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{code}
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\{code\}
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```
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Additional information:
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Only use the ones you need. If the TypedDict name conflicts with the imported object,
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rename the imported object NOT THE TYPE.
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My instructions: {description}
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My instructions: \{description\}
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We integrated this feature into the script builder by simply changing the "AI Gen" button to "AI Edit" button when code is selected.
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````
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Here's my python3 code:
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```python
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{code}
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\{code\}
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```
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Additional information:
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We have to export a "main" function and specify the parameter types but do not call it.
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````
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You fix the code shared by the user. Only output code. Wrap the code like that:
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```language
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Explain the error and the fix in the following format:
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explanation: "Here's the explanation"

‎blog/2023-11-15-launch-week-1/index.mdx

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- Docs - [Windmill AI for Flows](/docs/core_concepts/ai_generation#windmill-ai-for-flows)
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- Docs - [Windmill AI Code Completion](/docs/core_concepts/ai_generation#windmill-ai-code-completion)
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<DocCard
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title="Launch Week Day 1 - Flow Builder Copilot"
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description="Generate workflows from prompts."
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- Docs - [Local Development](/docs/advanced/local_development)
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title="Launch Week Day 1 - VS Code Extension"
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description="Write scripts & flows in your code editor with a UI preview."
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Once the configurations are done from the Windmill UI, the workers take their configuration dynamically, without having to restart.
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- Docs - [Dedicated Workers - High Throughput](/docs/core_concepts/dedicated_workers)
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Big claim today: We've benchmarked Windmill to be the fastest self-hostable generic workflow engine among Airflow, Prefect and even Temporal. For Airflow, there is a quite a margin, up to 10x faster!
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Big claim today: We've benchmarked Windmill to be the fastest self-hostable generic workflow engine among Airflow, Prefect and even Temporal. For Airflow, there is a quite a margin, up to 10x faster!
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You've known Windmill to be a productive environment to monitor, write and iterate on workflows, but we wanted to prove it's also the best system to deploy at scale in production.
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We are proud to introduce component groups, which allow you to build your own meta-components, accessible via drag-and-drop from any app in your workspace. Saved components group keep the logic between each components, avoiding creating it over and over.
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className="border-2 rounded-xl object-cover w-full h-full dark:border-gray-800"
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Once imported to Windmill, custom React components will be able to interact with the rest of the app.
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There are 3 main benefits of using your own components:
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### Windmill for data pipelines - S3 Integration
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![Windmill for data pipelines - S3 Integration](../2023-11-24-data-pipeline-orchestrator/data_pipelines.png.webp 'Windmill for data pipelines - S3 Integration')
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_Run your ETLs on-prem up to 5x faster using Windmill compared to Spark while simplifying your infra._
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### Restartable Flows
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![Restartable flows](../2023-11-24-restartable-flows/restartable_flows.png.webp 'Restartable flows')
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The principle is simple: once the flow is executed (whether in test or deployed), you have the possibility to restart it from any node (step, iteration of a for loop, or branch).
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You can unsubscribe at any time.

‎blog/2023-11-20-ai-flow-builder/index.mdx

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which prompts you with suggestions based on the code you're writing. This completes our already existing
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copilot which allows you to generate, edit and fix your scripts, learn more here:
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title="Windmill AI for Scripts"
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These features complement Windmill AI for Scripts, which lets you generate whole scripts using prompts and fix your errors automatically:
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We are using [GPT-4-turbo (1106-preview version)](https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo) for the AI Flow Builder and have changed Windmill AI for Scripts to use it too, as it is faster, three times cheaper and more up-to-date with libraries than GPT-4. We observed similar results to those obtained with GPT-4 in our tests, but we will continue to monitor the quality of the generations.
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Workflows are graphs ([DAG](https://en.wikipedia.org/wiki/Directed_acyclic_graph)) that represent the dependencies between steps.
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All the examples above focus on small api integrations but data pipeline that would usually be run on dedicated analytics engine are a great fit for Windmill when combined with s3, and dataframe/olap libraries such as polars or duckdb.
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To learn more about Windmill AI, please refer to our documentation:
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/>
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