Doc Update (#129)

* 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>

docs/source/getting_started/use_cases/function_calling.rst

@@ -1,71 +0,0 @@
-.. _arch_function_calling_agentic_guide:
-
-Agentic (Text-to-Action) Apps
-==============================
-
-Arch helps you easily personalize your applications by calling application-specific (API) functions
-via user prompts. This involves any predefined functions or APIs you want to expose to users to perform tasks,
-gather information, or manipulate data. This capability is generally referred to as **function calling**, where
-you have the flexibility to support “agentic” apps tailored to specific use cases - from updating insurance
-claims to creating ad campaigns - via prompts.
-
-Arch analyzes prompts, extracts critical information from prompts, engages in lightweight conversation with
-the user to gather any missing parameters and makes API calls so that you can focus on writing business logic.
-Arch does this via its purpose-built :ref:`Arch-FC LLM <llms_in_arch>` - the fastest (200ms p90 - 10x faser than GPT-4o)
-and cheapest (100x than GPT-40) function-calling LLM that matches performance with frontier models.
-______________________________________________________________________________________________
-
-.. image:: /_static/img/function-calling-network-flow.jpg
-   :width: 100%
-   :align: center
-
-
-Single Function Call
---------------------
-In the most common scenario, users will request a single action via prompts, and Arch efficiently processes the
-request by extracting relevant parameters, validating the input, and calling the designated function or API. Here
-is how you would go about enabling this scenario with Arch:
-
-Step 1: Define prompt targets with functions
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. literalinclude:: /_config/function-calling-network-agent.yml
-    :language: yaml
-    :linenos:
-    :emphasize-lines: 16-37
-    :caption: Define prompt targets that can enable users to engage with API and backened functions of an app
-
-Step 2: Process request parameters in Flask
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Once the prompt targets are configured as above, handling those parameters is
-
-.. literalinclude:: /_include/parameter_handling_flask.py
-    :language: python
-    :linenos:
-    :caption: Flask API example for parameter extraction via HTTP request parameters
-
-Parallel/ Multiple Function Calling
------------------------------------
-In more complex use cases, users may request multiple actions or need multiple APIs/functions to be called
-simultaneously or sequentially. With Arch, you can handle these scenarios efficiently using parallel or multiple
-function calling. This allows your application to engage in a broader range of interactions, such as updating
-different datasets, triggering events across systems, or collecting results from multiple services in one prompt.
-
-Arch-FC1B is built to manage these parallel tasks efficiently, ensuring low latency and high throughput, even
-when multiple functions are invoked. It provides two mechanisms to handle these cases:
-
-Step 1: Define Multiple Function Targets
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-When enabling multiple function calling, define the prompt targets in a way that supports multiple functions or
-API calls based on the user's prompt. These targets can be triggered in parallel or sequentially, depending on
-the user's intent.
-
-Example of Multiple Prompt Targets in YAML:
-
-.. literalinclude:: /_config/function-calling-network-agent.yml
-    :language: yaml
-    :linenos:
-    :emphasize-lines: 16-37
-    :caption: Define prompt targets that can enable users to engage with API and backened functions of an app