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Doc Update (#129)

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* init update

* Update terminology.rst

* fix the branch to create an index.html, and fix pre-commit issues

* Doc update

* made several changes to the docs after Shuguang's revision

* fixing pre-commit issues

* fixed the reference file to the final prompt config file

* added google analytics

---------

Co-authored-by: Salman Paracha <salmanparacha@MacBook-Pro-261.local>
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.. _quickstart:
Quickstart
================
Follow this guide to learn how to quickly set up Arch and integrate it into your generative AI applications.
Prerequisites
----------------------------
Before you begin, ensure you have the following:
.. vale Vale.Spelling = NO
- ``Docker`` & ``Python`` installed on your system
- ``API Keys`` for LLM providers (if using external LLMs)
The fastest way to get started using Arch is to use `katanemo/arch <https://hub.docker.com/r/katanemo/arch>`_ pre-built binaries.
You can also build it from source.
Step 1: Install Arch
----------------------------
Arch's CLI allows you to manage and interact with the Arch gateway efficiently. To install the CLI, simply
run the following command:
.. code-block:: console
$ pip install archgw
This will install the archgw command-line tool globally on your system.
.. tip::
We recommend that developers create a new Python virtual environment to isolate dependencies before installing Arch.
This ensures that `archgw` and its dependencies do not interfere with other packages on your system.
To create and activate a virtual environment, you can run the following commands:
.. code-block:: console
$ python -m venv venv
$ source venv/bin/activate # On Windows, use: venv\Scripts\activate
$ pip install archgw
Step 2: Config Arch
-------------------
Arch operates based on a configuration file where you can define LLM providers, prompt targets, and guardrails, etc.
Below is an example configuration to get you started, including:
.. vale Vale.Spelling = NO
- ``endpoints``: Specifies where Arch listens for incoming prompts.
- ``system_prompts``: Defines predefined prompts to set the context for interactions.
- ``llm_providers``: Lists the LLM providers Arch can route prompts to.
- ``prompt_guards``: Sets up rules to detect and reject undesirable prompts.
- ``prompt_targets``: Defines endpoints that handle specific types of prompts.
- ``error_target``: Specifies where to route errors for handling.
.. literalinclude:: includes/quickstart.yaml
:language: yaml
Step 3: Start Arch Gateway
--------------------------
.. code-block:: console
$ archgw up [path_to_config]
Next Steps
-------------------
Congratulations! You've successfully set up Arch and made your first prompt-based request. To further enhance your GenAI applications, explore the following resources:
- Full Documentation: Comprehensive guides and references.
- `GitHub Repository <https://github.com/katanemo/arch>`_: Access the source code, contribute, and track updates.
- `Support <https://github.com/katanemo/arch#contact>`_: Get help and connect with the Arch community .
With Arch, building scalable, fast, and personalized GenAI applications has never been easier. Dive deeper into Arch's capabilities and start creating innovative AI-driven experiences today!