chore: make docs folder structure resemble the one that appears on the website (#454)

## Description

Changes:
- Updated folder structure, now reflects a 1:1 match between the order
of files in the project directory and their appearance on the website,
as described in the [Docusaurus sidebar - using number
prefixes](https://docusaurus.io/docs/next/sidebar/autogenerated#using-number-prefixes).
- Adjusted links to point to the correct locations in our repository.

### Type of change

- [ ] Bug fix (non-breaking change which fixes an issue)
- [ ] New feature (non-breaking change which adds functionality)
- [ ] Breaking change (fix or feature that would cause existing
functionality to not work as expected)
- [x] Documentation update (improves or adds clarity to existing
documentation)

### Checklist

- [x] I have performed a self-review of my code
- [x] I have commented my code, particularly in hard-to-understand areas
- [x] I have updated the documentation accordingly
- [x] My changes generate no new warnings

Author: jakmro

docs/docs/fundamentals/getting-started.md …cs/01-fundamentals/01-getting-started.mddocs/docs/fundamentals/getting-started.md renamed to docs/docs/01-fundamentals/01-getting-started.md docs/docs/fundamentals/getting-started.md renamed to docs/docs/01-fundamentals/01-getting-started.md

@@ -1,5 +1,5 @@
 ---
-title: Loading models
+title: Loading Models
 ---
 
 There are three different methods available for loading model files, depending on their size and location.

docs/docs/fundamentals/loading-models.md …ocs/01-fundamentals/02-loading-models.mddocs/docs/fundamentals/loading-models.md renamed to docs/docs/01-fundamentals/02-loading-models.md docs/docs/fundamentals/loading-models.md renamed to docs/docs/01-fundamentals/02-loading-models.md

@@ -1,5 +1,5 @@
 ---
-title: Frequently asked questions
+title: Frequently Asked Questions
 ---
 
 This section is meant to answer some common community inquiries, especially regarding the ExecuTorch runtime or adding your own models. If you can't see an answer to your question, feel free to open up a [discussion](https://github.com/software-mansion/react-native-executorch/discussions/new/choose).
@@ -10,11 +10,11 @@ Each hook documentation subpage (useClassification, useLLM, etc.) contains a sup
 
 ### How can I run my own AI model?
 
-To run your own model, you need to directly access the underlying [ExecuTorch Module API](https://pytorch.org/executorch/stable/extension-module.html). We provide an experimental [React hook](../executorch-bindings/useExecutorchModule.md) along with a [TypeScript alternative](../typescript-api/ExecutorchModule.md), which serve as a way to use the aforementioned API without the need of diving into native code. In order to get a model in a format runnable by the runtime, you'll need to get your hands dirty with some ExecuTorch knowledge. For more guides on exporting models, please refer to the [ExecuTorch tutorials](https://pytorch.org/executorch/stable/tutorials/export-to-executorch-tutorial.html). Once you obtain your model in a `.pte` format, you can run it with `useExecuTorchModule` and `ExecuTorchModule`.
+To run your own model, you need to directly access the underlying [ExecuTorch Module API](https://pytorch.org/executorch/stable/extension-module.html). We provide an experimental [React hook](../02-hooks/03-executorch-bindings/useExecutorchModule.md) along with a [TypeScript alternative](../03-typescript-api/03-executorch-bindings/ExecutorchModule.md), which serve as a way to use the aforementioned API without the need of diving into native code. In order to get a model in a format runnable by the runtime, you'll need to get your hands dirty with some ExecuTorch knowledge. For more guides on exporting models, please refer to the [ExecuTorch tutorials](https://pytorch.org/executorch/stable/tutorials/export-to-executorch-tutorial.html). Once you obtain your model in a `.pte` format, you can run it with `useExecuTorchModule` and `ExecuTorchModule`.
 
 ### Can you do function calling with useLLM?
 
-If your model supports tool calling (i.e. its chat template can process tools) you can use the method explained in [useLLM page](../natural-language-processing/useLLM.md#tool-calling).
+If your model supports tool calling (i.e. its chat template can process tools) you can use the method explained on the [useLLM page](../02-hooks/01-natural-language-processing/useLLM.md).
 
 If your model doesn't support it, you can still work around it using context. For details, refer to [this comment](https://github.com/software-mansion/react-native-executorch/issues/173#issuecomment-2775082278).
 

…s/docs/faq/frequently-asked-questions.md …mentals/03-frequently-asked-questions.mddocs/docs/faq/frequently-asked-questions.md renamed to docs/docs/01-fundamentals/03-frequently-asked-questions.md docs/docs/faq/frequently-asked-questions.md renamed to docs/docs/01-fundamentals/03-frequently-asked-questions.md

@@ -0,0 +1,6 @@
+{
+  "label": "Fundamentals",
+  "link": {
+    "type": "generated-index"
+  }
+}

docs/docs/01-fundamentals/_category_.json

@@ -0,0 +1,6 @@
+{
+  "label": "Natural Language Processing",
+  "link": {
+    "type": "generated-index"
+  }
+}

docs/docs/02-hooks/01-natural-language-processing/_category_.json

@@ -23,7 +23,7 @@ description: "Learn how to use LLMs in your React Native applications with React
 
 React Native ExecuTorch supports a variety of LLMs (checkout our [HuggingFace repository](https://huggingface.co/software-mansion) for model already converted to ExecuTorch format) including Llama 3.2. Before getting started, you’ll need to obtain the .pte binary—a serialized model, the tokenizer and tokenizer config JSON files. There are various ways to accomplish this:
 
-- For your convenience, it's best if you use models exported by us, you can get them from our [HuggingFace repository](https://huggingface.co/software-mansion). You can also use [constants](https://github.com/software-mansion/react-native-executorch/tree/main/src/constants/modelUrls.ts) shipped with our library.
+- For your convenience, it's best if you use models exported by us, you can get them from our [HuggingFace repository](https://huggingface.co/software-mansion). You can also use [constants](https://github.com/software-mansion/react-native-executorch/blob/main/packages/react-native-executorch/src/constants/modelUrls.ts) shipped with our library.
 - Follow the official [tutorial](https://github.com/pytorch/executorch/blob/fe20be98c/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md) made by ExecuTorch team to build the model and tokenizer yourself.
 
 :::danger
@@ -59,7 +59,7 @@ The code snippet above fetches the model from the specified URL, loads it into m
 
 ### Arguments
 
-**`modelSource`** - `ResourceSource` that specifies the location of the model binary. For more information, take a look at [loading models](../fundamentals/loading-models.md) section.
+**`modelSource`** - `ResourceSource` that specifies the location of the model binary. For more information, take a look at [loading models](../../01-fundamentals/02-loading-models.md) section.
 
 **`tokenizerSource`** - `ResourceSource` pointing to the JSON file which contains the tokenizer.
 

…cs/natural-language-processing/useLLM.md …01-natural-language-processing/useLLM.mddocs/docs/natural-language-processing/useLLM.md renamed to docs/docs/02-hooks/01-natural-language-processing/useLLM.md docs/docs/natural-language-processing/useLLM.md renamed to docs/docs/02-hooks/01-natural-language-processing/useLLM.md

@@ -25,7 +25,7 @@ Currently, we do not support direct microphone input streaming to the model. Ins
 :::
 
 :::caution
-It is recommended to use models provided by us, which are available at our [Hugging Face repository](https://huggingface.co/software-mansion/react-native-executorch-moonshine-tiny). You can also use [constants](https://github.com/software-mansion/react-native-executorch/tree/main/src/constants/modelUrls.ts) shipped with our library.
+It is recommended to use models provided by us, which are available at our [Hugging Face repository](https://huggingface.co/software-mansion/react-native-executorch-moonshine-tiny). You can also use [constants](https://github.com/software-mansion/react-native-executorch/blob/main/packages/react-native-executorch/src/constants/modelUrls.ts) shipped with our library.
 :::
 
 ## Reference
@@ -72,13 +72,13 @@ Given that STT models can process audio no longer than 30 seconds, there is a ne
 A literal of `"moonshine" | "whisper" | "whisperMultilingual` which serves as an identifier for which model should be used.
 
 **`encoderSource?`**
-A string that specifies the location of a .pte file for the encoder. For further information on passing model sources, check out [Loading Models](https://docs.swmansion.com/react-native-executorch/docs/fundamentals/loading-models). Defaults to [constants](https://github.com/software-mansion/react-native-executorch/blob/main/src/constants/modelUrls.ts) for given model.
+A string that specifies the location of a .pte file for the encoder. For further information on passing model sources, check out [Loading Models](../../01-fundamentals/02-loading-models.md). Defaults to [constants](https://github.com/software-mansion/react-native-executorch/blob/main/packages/react-native-executorch/src/constants/modelUrls.ts) for given model.
 
 **`decoderSource?`**
-Analogous to the encoderSource, this takes in a string which is a source for the decoder part of the model. Defaults to [constants](https://github.com/software-mansion/react-native-executorch/blob/main/src/constants/modelUrls.ts) for given model.
+Analogous to the encoderSource, this takes in a string which is a source for the decoder part of the model. Defaults to [constants](https://github.com/software-mansion/react-native-executorch/blob/main/packages/react-native-executorch/src/constants/modelUrls.ts) for given model.
 
 **`tokenizerSource?`**
-A string that specifies the location to the tokenizer for the model. This works just as the encoder and decoder do. Defaults to [constants](https://github.com/software-mansion/react-native-executorch/blob/main/src/constants/modelUrls.ts) for given model.
+A string that specifies the location to the tokenizer for the model. This works just as the encoder and decoder do. Defaults to [constants](https://github.com/software-mansion/react-native-executorch/blob/main/packages/react-native-executorch/src/constants/modelUrls.ts) for given model.
 
 **`overlapSeconds?`**
 Specifies the length of overlap between consecutive audio chunks (expressed in seconds). Overrides `streamingConfig` argument.
@@ -197,7 +197,7 @@ enum SpeechToTextLanguage {
 
 ## Running the model
 
-Before running the model's `transcribe` method be sure to obtain waveform of the audio You wish to transcribe. You need to obtain the waveform from audio on your own (remember to use sampling rate of 16kHz!), in the snippet above we provide an example how you can do that. In the latter case just pass the obtained waveform as argument to the `transcribe` method which returns a promise resolving to the generated tokens when successful. If the model fails during inference the `error` property contains details of the error. If you want to obtain tokens in a streaming fashion, you can also use the sequence property, which is updated with each generated token, similar to the [useLLM](../natural-language-processing/useLLM.md) hook.
+Before running the model's `transcribe` method be sure to obtain waveform of the audio You wish to transcribe. You need to obtain the waveform from audio on your own (remember to use sampling rate of 16kHz!), in the snippet above we provide an example how you can do that. In the latter case just pass the obtained waveform as argument to the `transcribe` method which returns a promise resolving to the generated tokens when successful. If the model fails during inference the `error` property contains details of the error. If you want to obtain tokens in a streaming fashion, you can also use the sequence property, which is updated with each generated token, similar to the [useLLM](../../02-hooks/01-natural-language-processing/useLLM.md) hook.
 
 #### Multilingual transcription
 

…l-language-processing/useSpeechToText.md …l-language-processing/useSpeechToText.mddocs/docs/natural-language-processing/useSpeechToText.md renamed to docs/docs/02-hooks/01-natural-language-processing/useSpeechToText.md docs/docs/natural-language-processing/useSpeechToText.md renamed to docs/docs/02-hooks/01-natural-language-processing/useSpeechToText.md

@@ -18,7 +18,7 @@ description: "Learn how to use text embeddings models in your React Native appli
 Text Embedding is the process of converting text into a numerical representation. This representation can be used for various natural language processing tasks, such as semantic search, text classification, and clustering.
 
 :::caution
-It is recommended to use models provided by us, which are available at our [Hugging Face repository](https://huggingface.co/software-mansion/react-native-executorch-all-MiniLM-L6-v2). You can also use [constants](https://github.com/software-mansion/react-native-executorch/tree/main/src/constants/modelUrls.ts) shipped with our library.
+It is recommended to use models provided by us, which are available at our [Hugging Face repository](https://huggingface.co/software-mansion/react-native-executorch-all-MiniLM-L6-v2). You can also use [constants](https://github.com/software-mansion/react-native-executorch/blob/main/packages/react-native-executorch/src/constants/modelUrls.ts) shipped with our library.
 :::
 
 ## Reference
@@ -45,7 +45,7 @@ try {
 ### Arguments
 
 **`modelSource`**
-A string that specifies the location of the model binary. For more information, take a look at [loading models](../fundamentals/loading-models.md) page.
+A string that specifies the location of the model binary. For more information, take a look at [loading models](../../01-fundamentals/02-loading-models.md) page.
 
 **`tokenizerSource`**
 A string that specifies the location of the tokenizer JSON file.

…language-processing/useTextEmbeddings.md …language-processing/useTextEmbeddings.mddocs/docs/natural-language-processing/useTextEmbeddings.md renamed to docs/docs/02-hooks/01-natural-language-processing/useTextEmbeddings.md docs/docs/natural-language-processing/useTextEmbeddings.md renamed to docs/docs/02-hooks/01-natural-language-processing/useTextEmbeddings.md

@@ -0,0 +1,6 @@
+{
+  "label": "Computer Vision",
+  "link": {
+    "type": "generated-index"
+  }
+}