Add subsection for docs; prioritize getting started with container (#962

)

README.md

@@ -63,10 +63,65 @@ Checkout the [`unstructured-api` repo](https://github.com/Unstructured-IO/unstru
 to get started making API calls.
 You’ll also find instructions there about how to host your own version of the API.
 
+## :bookmark: Documentation
+This README gives an overview of how to install, use and develop the library.
+For more comprehensive documentation, visit https://unstructured-io.github.io/unstructured/ .
+
 ## :eight_pointed_black_star: Quick Start
 
+There are two ways to use the library: 1) run a container or 2) install it
+
+### Using the library in a container
+
+The following instructions are intended to help you get up and running using Docker to interact with `unstructured`.
+See [here](https://docs.docker.com/get-docker/) if you don't already have docker installed on your machine.
+
+NOTE: we build multi-platform images to support both x86_64 and Apple silicon hardware. `docker pull` should download the corresponding image for your architecture, but you can specify with `--platform` (e.g. `--platform linux/amd64`) if needed.
+
+We build Docker images for all pushes to `main`. We tag each image with the corresponding short commit hash (e.g. `fbc7a69`) and the application version (e.g. `0.5.5-dev1`). We also tag the most recent image with `latest`. To leverage this, `docker pull` from our image repository.
+
+```bash
+docker pull quay.io/unstructured-io/unstructured:latest
+```
+
+Once pulled, you can create a container from this image and shell to it.
+
+```bash
+# create the container
+docker run -dt --name unstructured quay.io/unstructured-io/unstructured:latest
+
+# this will drop you into a bash shell where the Docker image is running
+docker exec -it unstructured bash
+```
+
+You can also build your own Docker image.
+
+If you only plan on parsing one type of data you can speed up building the image by commenting out some
+of the packages/requirements necessary for other data types. See Dockerfile to know which lines are necessary
+for your use case.
+
+```bash
+make docker-build
+
+# this will drop you into a bash shell where the Docker image is running
+make docker-start-bash
+```
+
+Once in the running container, you can try things out directly in Python interpreter's interactive mode.
+```bash
+# this will drop you into a python console so you can run the below partition functions
+python3
+
+>>> from unstructured.partition.pdf import partition_pdf
+>>> elements = partition_pdf(filename="example-docs/layout-parser-paper-fast.pdf")
+
+>>> from unstructured.partition.text import partition_text
+>>> elements = partition_text(filename="example-docs/fake-text.txt")
+```
+
+### Installing the library
 Use the following instructions to get up and running with `unstructured` and test your
-installation. NOTE: We do not currently support python 3.11, please use an older version.
+installation.
 
 - Install the Python SDK with `pip install "unstructured[local-inference]"`
 		- If you do not need to process PDFs or images, you can run `pip install unstructured`
@@ -76,10 +131,10 @@ installation. NOTE: We do not currently support python 3.11, please use an older
     - `poppler-utils` (images and PDFs)
     - `tesseract-ocr` (images and PDFs)
     - `libreoffice` (MS Office docs)
+    - `pandoc` (EPUBs, RTFs and Open Office docs)
 
-- If you are parsing PDFs and want to use a model from the [layoutparser model
-  zoo](https://github.com/Unstructured-IO/unstructured-inference#using-models-from-the-layoutparser-model-zoo),
-  use the instructions [here](https://github.com/Unstructured-IO/unstructured-inference#detectron2).
+- For suggestions on how to install on Windows and to learn about dependencies for other features, see the
+  installation documentation [here](https://unstructured-io.github.io/unstructured/installing.html).
 
 At this point, you should be able to run the following code:
 
@@ -122,55 +177,6 @@ about the library.
 
 
 
-## :dizzy: Instructions for using the docker image
-
-The following instructions are intended to help you get up and running using Docker to interact with `unstructured`.
-See [here](https://docs.docker.com/get-docker/) if you don't already have docker installed on your machine.
-
-NOTE: we build multi-platform images to support both x86_64 and Apple silicon hardware. `docker pull` should download the corresponding image for your architecture, but you can specify with `--platform` (e.g. `--platform linux/amd64`) if needed.
-
-We build Docker images for all pushes to `main`. We tag each image with the corresponding short commit hash (e.g. `fbc7a69`) and the application version (e.g. `0.5.5-dev1`). We also tag the most recent image with `latest`. To leverage this, `docker pull` from our image repository.
-
-```bash
-docker pull quay.io/unstructured-io/unstructured:latest
-```
-
-Once pulled, you can create a container from this image and shell to it.
-
-```bash
-# create the container
-docker run -dt --name unstructured quay.io/unstructured-io/unstructured:latest
-
-# this will drop you into a bash shell where the Docker image is running
-docker exec -it unstructured bash
-```
-
-You can also build your own Docker image.
-
-If you only plan on parsing one type of data you can speed up building the image by commenting out some
-of the packages/requirements necessary for other data types. See Dockerfile to know which lines are necessary
-for your use case.
-
-```bash
-make docker-build
-
-# this will drop you into a bash shell where the Docker image is running
-make docker-start-bash
-```
-
-Once in the running container, you can try things out directly in Python interpreter's interactive mode.
-```bash
-# this will drop you into a python console so you can run the below partition functions
-python3
-
->>> from unstructured.partition.pdf import partition_pdf
->>> elements = partition_pdf(filename="example-docs/layout-parser-paper-fast.pdf")
-
->>> from unstructured.partition.text import partition_text
->>> elements = partition_text(filename="example-docs/fake-text.txt")
-```
-
-
 ## :coffee: Installation Instructions for Local Development
 
 The following instructions are intended to help you get up and running with `unstructured`