docs: added fetching from notion (#2670)

* Added new Docusaurus instance that fetches automatically from Notion

* Add Github workflow to fetch docs from Notion

* Added legacy peer deps to solve dependency problems

* Fix git ignore and added pages

docs/docs/Contributing/_category_.json

@@ -0,0 +1 @@
+{"position":10, "label":"Contributing"}

docs/docs/Contributing/contributing-community.md

@@ -0,0 +1,55 @@
+---
+title: Community
+sidebar_position: 3
+slug: /contributing-community
+---
+
+
+
+## 🤖 Join **Langflow** Discord server {#80011e0bda004e83a8012c7ec6eab29a}
+
+
+Join us to ask questions and showcase your projects.
+
+
+Let's bring together the building blocks of AI integration!
+
+
+Langflow [Discord](https://discord.gg/EqksyE2EX9) server.
+
+
+---
+
+
+## 🐦 Stay tuned for **Langflow** on Twitter {#6a17ba5905ad4f7aa5347af7854779f6}
+
+
+Follow [@langflow_ai](https://twitter.com/langflow_ai) on **Twitter** to get the latest news about **Langflow**.
+
+
+---
+
+
+## ⭐️ Star **Langflow** on GitHub {#c903a569934643799bf52b7d1b3514e1}
+
+
+You can "star" **Langflow** in [GitHub](https://github.com/langflow-ai/langflow).
+
+
+By adding a star, other users will be able to find it more easily and see that it has been already useful for others.
+
+
+---
+
+
+## 👀 Watch the GitHub repository for releases {#d0a089ed717742308bd17430e5ae6309}
+
+
+You can "watch" **Langflow** in [GitHub](https://github.com/langflow-ai/langflow). If you select "Watching" instead of "Releases only" you will receive notifications when someone creates a new issue or question. You can also specify that you only want to be notified about new issues, discussions, PRs, etc. so you can try and help them solve those questions.
+
+
+---
+
+
+Thanks! 🚀
+

docs/docs/Contributing/contributing-github-issues.md

@@ -0,0 +1,21 @@
+---
+title: GitHub Issues
+sidebar_position: 2
+slug: /contributing-github-issues
+---
+
+
+
+Our [issues](https://github.com/langflow-ai/langflow/issues) page is kept up to date with bugs, improvements, and feature requests. There is a taxonomy of labels to help with sorting and discovery of issues of interest.
+
+
+If you're looking for help with your code, consider posting a question on the [GitHub Discussions board](https://github.com/langflow-ai/langflow/discussions). Please understand that we won't be able to provide individual support via email. We also believe that help is much more valuable if it's **shared publicly**, so that more people can benefit from it.
+
+- **Describing your issue:** Try to provide as many details as possible. What exactly goes wrong? _How_ is it failing? Is there an error? "XY doesn't work" usually isn't that helpful for tracking down problems. Always remember to include the code you ran and if possible, extract only the relevant parts and don't just dump your entire script. This will make it easier for us to reproduce the error.
+- **Sharing long blocks of code or logs:** If you need to include long code, logs or tracebacks, you can wrap them in `<details>` and `</details>`. This [collapses the content](https://developer.mozilla.org/en/docs/Web/HTML/Element/details) so it only becomes visible on click, making the issue easier to read and follow.
+
+## Issue labels {#e19eae656c914ce7aedc4f55565cc0bc}
+
+
+[See this page](https://github.com/langflow-ai/langflow/labels) for an overview of the system we use to tag our issues and pull requests.
+

docs/docs/Contributing/contributing-how-to-contribute.md

@@ -0,0 +1,141 @@
+---
+title: How to contribute?
+sidebar_position: 1
+slug: /contributing-how-to-contribute
+---
+
+
+
+👋 Hello there! We welcome contributions from developers of all levels to our open-source project on [GitHub](https://github.com/langflow-ai/langflow). If you'd like to contribute, please check our contributing guidelines and help make Langflow more accessible.
+
+
+As an open-source project in a rapidly developing field, we are extremely open to contributions, whether in the form of a new feature, improved infra, or better documentation.
+
+
+To contribute to this project, please follow a ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow.
+
+
+Please do not try to push directly to this repo unless you are a maintainer.
+
+
+---
+
+
+## Local development {#0388cc3c758d434d994022863a6bafa9}
+
+
+You can develop Langflow using docker compose, or locally.
+
+
+We provide a `.vscode/launch.json` file for debugging the backend in VSCode, which is a lot faster than using docker compose.
+
+
+Setting up hooks:
+
+
+`make init`
+
+
+This will install the pre-commit hooks, which will run `make format` on every commit.
+
+
+It is advised to run `make lint` before pushing to the repository.
+
+
+---
+
+
+## Run locally {#5225c2ef0cd6403c9f6c6bbd888115e0}
+
+
+Langflow can run locally by cloning the repository and installing the dependencies. We recommend using a virtual environment to isolate the dependencies from your system.
+
+
+Before you start, make sure you have the following installed:
+
+- Poetry (>=1.4)
+- Node.js
+
+Then, in the root folder, install the dependencies and start the development server for the backend:
+
+
+`make backend`
+
+
+And the frontend:
+
+
+`make frontend`
+
+
+---
+
+
+## Docker compose {#b07f359414ff4220ac615afc364ee46e}
+
+
+The following snippet will run the backend and frontend in separate containers. The frontend will be available at `localhost:3000` and the backend at `localhost:7860`.
+
+
+`docker compose up --build# ormake dev build=1`
+
+
+---
+
+
+## Documentation {#5f34bcaeccdc4489b0c5ee2c4a21354e}
+
+
+The documentation is built using [Docusaurus](https://docusaurus.io/). To run the documentation locally, run the following commands:
+
+
+`cd docsnpm installnpm run start`
+
+
+The documentation will be available at `localhost:3000` and all the files are located in the `docs/docs` folder. Once you are done with your changes, you can create a Pull Request to the `main` branch.
+
+
+## Submitting Components {#9676353bc4504551a4014dd572ac8be8}
+
+
+---
+
+
+New components are added as objects of the [CustomComponent](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/interface/custom/custom_component/custom_component.py) class and any dependencies are added to the [pyproject.toml](https://github.com/langflow-ai/langflow/blob/dev/pyproject.toml#L27) file.
+
+
+## Add an example component {#8caae106c853465d83183e7f5272e4d8}
+
+
+You have a new document loader called **MyCustomDocumentLoader** and it would look awesome in Langflow.
+
+1. Write your loader as an object of the [CustomComponent](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/interface/custom/custom_component/custom_component.py) class. You'll create a new class, `MyCustomDocumentLoader`, that will inherit from `CustomComponent` and override the base class's methods.
+2. Define optional attributes like `display_name`, `description`, and `documentation` to provide information about your custom component.
+3. Implement the `build_config` method to define the configuration options for your custom component.
+4. Implement the `build` method to define the logic for taking input parameters specified in the `build_config` method and returning the desired output.
+5. Add the code to the [/components/documentloaders](https://github.com/langflow-ai/langflow/tree/dev/src/backend/base/langflow/components) folder.
+6. Add the dependency to [/documentloaders/__init__.py](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/components/documentloaders/__init__.py) as `from .MyCustomDocumentLoader import MyCustomDocumentLoader`.
+7. Add any new dependencies to the outer [pyproject.toml](https://github.com/langflow-ai/langflow/blob/dev/pyproject.toml#L27) file.
+8. Submit documentation for your component. For this example, you'd submit documentation to the [loaders page](https://github.com/langflow-ai/langflow/blob/dev/docs/docs/components/loaders).
+9. Submit your changes as a pull request. The Langflow team will have a look, suggest changes, and add your component to Langflow.
+
+## User Sharing {#34ac32e11f344eab892b94531a21d2c9}
+
+
+You might want to share and test your custom component with others, but don't need it merged into the main source code.
+
+
+If so, you can share your component on the Langflow store.
+
+1. [Register at the Langflow store](https://www.langflow.store/login/).
+2. Undergo pre-validation before receiving an API key.
+3. To deploy your amazing component directly to the Langflow store, without it being merged into the main source code, navigate to your flow, and then click **Share**. The share window appears:
+
+![](./683296796.png)
+
+
+4. Choose whether you want to flow to be public or private. You can also **Export** your flow as a JSON file from this window. When you're ready to share the flow, click **Share Flow**. You should see a **Flow shared successfully** popup.
+
+
+5. To confirm, navigate to the **Langflow Store** and filter results by **Created By Me**. You should see your new flow on the **Langflow Store**.
+

docs/docs/Contributing/contributing-telemetry.md

@@ -0,0 +1,59 @@
+---
+title: Telemetry
+sidebar_position: 0
+slug: /contributing-telemetry
+---
+
+
+
+Our system uses anonymous telemetry to collect essential usage statistics to enhance functionality and user experience. This data helps us identify commonly used features and areas needing improvement, ensuring our development efforts align with what you need.
+
+
+INFO
+
+
+We respect your privacy and are committed to protecting your data. We do not collect any personal information or sensitive data. All telemetry data is anonymized and used solely for improving Langflow.
+
+
+You can opt-out of telemetry by setting the `LANGFLOW_DO_NOT_TRACK` or `DO_NOT_TRACK` environment variable to `true` before running Langflow. This will disable telemetry data collection.
+
+
+## Data Collected Includes: {#1734ed50fb4a4a45aaa84185b44527ca}
+
+
+### Run {#2d427dca4f0148ae867997f6789e8bfb}
+
+- **IsWebhook**: Indicates whether the operation was triggered via a webhook.
+- **Seconds**: Duration in seconds for how long the operation lasted, providing insights into performance.
+- **Success**: Boolean value indicating whether the operation was successful, helping identify potential errors or issues.
+- **ErrorMessage**: Provides error message details if the operation was unsuccessful, aiding in troubleshooting and enhancements.
+
+### Shutdown {#081e4bd4faec430fb05b657026d1a69c}
+
+- **Time Running**: Total runtime before shutdown, useful for understanding application lifecycle and optimizing uptime.
+
+### Version {#dc09f6aba6c64c7b8dad3d86a7cba6d6}
+
+- **Version**: The specific version of Langflow used, which helps in tracking feature adoption and compatibility.
+- **Platform**: Operating system of the host machine, which aids in focusing our support for popular platforms like Windows, macOS, and Linux.
+- **Python**: The version of Python used, assisting in maintaining compatibility and support for various Python versions.
+- **Arch**: Architecture of the system (e.g., x86, ARM), which helps optimize our software for different hardware.
+- **AutoLogin**: Indicates whether the auto-login feature is enabled, reflecting user preference settings.
+- **CacheType**: Type of caching mechanism used, which impacts performance and efficiency.
+- **BackendOnly**: Boolean indicating whether you are running Langflow in a backend-only mode, useful for understanding deployment configurations.
+
+### Playground {#ae6c3859f612441db3c15a7155e9f920}
+
+- **Seconds**: Duration in seconds for playground execution, offering insights into performance during testing or experimental stages.
+- **ComponentCount**: Number of components used in the playground, which helps understand complexity and usage patterns.
+- **Success**: Success status of the playground operation, aiding in identifying the stability of experimental features.
+
+### Component {#630728d6654c40a6b8901459a4bc3a4e}
+
+- **Name**: Identifies the component, providing data on which components are most utilized or prone to issues.
+- **Seconds**: Time taken by the component to execute, offering performance metrics.
+- **Success**: Whether the component operated successfully, which helps in quality control.
+- **ErrorMessage**: Details of any errors encountered, crucial for debugging and improvement.
+
+This telemetry data is crucial for enhancing Langflow and ensuring that our development efforts align with your needs. Your feedback and suggestions are invaluable in shaping the future of Langflow, and we appreciate your support in making Langflow better for everyone.
+